Airway tree segmentation based on CT images of patients with acute respiratory distress syndrome (ARDS)

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(1)Airway Tree Segmentation Based on CT Images of Patients with Acute Respiratory Distress Syndrome (ARDS). by Diana Carolina Ortega. Submitted in satisfaction of the requirements for the degree of Master EEAP - Parcours Systemes et Images and Maestrı́a en Ingenieria de Sistemas y Computación. SUPERVISORS Marcela Hernández Hoyos Maciej Orkisz. December 2012.

(2) “To my family...”.

(3) Acknowledgements I would like to thank my supervisors, Prof. Maciej Orkisz, and Prof. Marcela Hernández, for their patience, guidance and encouragement during the last year. MD PhD Jean Chistophe Richard for his collaboration during the project. To my family for their encouragement and finally, i would like to thank Fabian, and all my friends, for their support and help across the project. . . .. ii.

(4) Contents Acknowledgements. ii. List of Figures. iv. 1 Introduction. 2. 2 Medical context. 4. 3 State of Art. 7. 4 Method 11 4.1 Lung Parenchyma Segmentation . . . . . . . . . . . . . . . . . . . . . . . 12 4.2 Airway Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 4.3 Aeration Levels and PV Curves . . . . . . . . . . . . . . . . . . . . . . . . 16 5 Results and Discussion 5.1 Lung Parenchyma Segmentation 5.2 Airway Segmentation . . . . . . . 5.3 Aeration Levels and PV Curves . 5.3.1 Over-aerated Class . . . . 5.3.2 Normal-aerated class . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. 18 18 20 22 24 25. 6 Conclusions. 27. Bibliography. 29. iii.

(5) List of Figures 2.1 2.2. Pulmonary anatomy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CT images with and without ARDS . . . . . . . . . . . . . . . . . . . . .. 4 5. 3.1. PV curves with and without ARDS . . . . . . . . . . . . . . . . . . . . . .. 8. 4.1 4.2. Lung Segmentation usingTurtleSeg. . . . . . . . . . . . . . . . . . . . . . . Algorithm used for bronchi segmentation. a) Initial region growing segmentation. b) Finding tree’s endings in the initial segmentation. c)Defining VOI’s for enclosing initial segmentation endings. d) Results of the VRG segmentation algorithm inside the VOIs . . . . . . . . . . . . . . . . . . . Color map for each zone in the lung region . . . . . . . . . . . . . . . . . Representation of the principal points of the PV curve . . . . . . . . . .. 12. 4.3 4.4 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9. Lung Segmentation usingTurtleSeg. . . . . . . . . . . . . . . . . . . . . . Lung Segmentation using TurtleSeg. Some pixels missing. . . . . . . . . Curves of the amount of threshold vs. segmented voxels . . . . . . . . . Airway masks representation . . . . . . . . . . . . . . . . . . . . . . . . Problem of leakage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Segmentation results with the novel algorithm . . . . . . . . . . . . . . . PV curve for Pig01. Initial counting of all voxels . . . . . . . . . . . . . PV curve for Pig 17. Initial counting of all voxels . . . . . . . . . . . . . Over-aerated class Pig 01. Initial counting of all voxels in the overaerated region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.10 Overaeratedcclass Pig01. Bronchi initial counting of all voxels in the over-aerated region for bronchi . . . . . . . . . . . . . . . . . . . . . . . 5.11 Normally-aerated class Pig01. Bronchi initial counting of all voxels in the normally-aerated region . . . . . . . . . . . . . . . . . . . . . . . . . . .. iv. . . . . . . . .. 15 16 17 18 19 20 21 21 23 23 24. . 24 . 25 . 26.

(6) Abstract The purpose of this project was to study the influence of the airway volume over the lung volume for acute respiratory distress syndrome (ARDS). For achieving this, a method of region growing and Variational Region Growing (VRG) addapted for airway segmentation were tested and voxels of segmentation results were classified into regions depending on the aeration level as overaerated, normally aerated, poorly aerated or non-aerated. Finally, results of each region for region growing method were compared against results for VRG method using Pressure-volume curves (PV-curves). Tests were carried out over Computer Tomography (CT) volumes for 8 pigs with induced ARDS at various aeration conditions. A total of 37 CT images were analyzed for pig. The results indicated that VRG method goes to the 4 or 5 generation of airway and segments 14% more of bronchi in pathologic lungs than a region growing algorithm. It was found that most of the bronchi reside in the overaerated class and they represent 2% of the lung area in the pig. Despite those results, it is recommended further research to improve accuracy of airway detection and a technique for results validation and branch counting..

(7) Chapter 1. Introduction Acute Respiratory Distress Syndrome (ARDS) is a life-threatening disease. Its mortality remains high, despite advances in intensive care. Lung volume calculation in ARDS is an important challenge for helping physicians to find a novel therapeutic strategy that helps them save lives. Computer Tomography (CT) imagery has been an important tool for diagnosis and research; however, risks associated to radiation in CT images and the difficulty in moving the patient, make it necessary to look for new techniques. In this sense, Electrical Impedance Tomography (EIT) appears as a promising imaging technique because is noninvasive, dynamic, and radiation free. This technique has some limitations, as for example, the low resolution of the images. A PhD thesis has been proposed in order to overcome those limitations. In the latter thesis CT images are used for accurate lung volume measuring, as reference for the development of a novel strategy that uses EIT for diagnosis and monitoring and for improving treatment techniques. Automatic measurement of air volume of pathologic lungs in CT images is not an easy task. Besides the loss of contrast due to the disease, volume of airway and arterial trees affects the outcome. This document shows the effort around airway tree segmentation and measurement of the volume inside the lung. Several approaches go from simple seeded region growing algorithms to the use of a-priory knowledge to detect more bronchi in healthy lungs. As ARDS changes lung visual characteristics, it is important to implement a segmentation method for handling bronchi inside pathological lungs and measure the impact of the airway volume in the treatment of the disease. For achieving this, information of several CT images at different conditions of ventilation has been. 2.

(8) Airway tree segmentation based on CT images in patients with (ARDS). 3. analyzed and an existing method has been improved in order to segment bronchi when there exist ARDS. This project was developed as a final master project, in agreement between La Universidad de los Andes in Colombia and Université Claude Bernard Lyon 1 in France. It was carried out in joint supervision between Maciej Orkisz from Creatis Laboratory in Lyon, France, and Marcela Hernández Hoyos, from the Imagine group in La Universidad de los Andes in Bogotá, Colombia. Background information about lung anatomy and ARDS is provided in Chapter 2. Theory underlying previous work in air volume quantification and airway segmentation is described in Chapter 3; then, methods implemented for parenchyma segmentation, bronchi segmentation, and volume quantification are described in Chapter 4. In Chapter 5, results of the implemented method are presented and discussed. Finally, in Chapter 6, conclusions are drawn..

(9) Chapter 2. Medical context The lungs consist of approximately 1% of respiratory airways in a sponge-like structure, which comprises large vessels and parenchyma (small vessels and bronchioles, interalveolar septa, and alveolar airspaces) [1], laying in a pleural sac. Fissures divide each lung into lobes (see Fig. 2.1 a) ). An oblique fissure separates the upper and the lower lobe in the left lung. An oblique and and horizontal fissures divides the right lung in the upper, middle, and lower lobes [2]. They are approximately cone-shaped structures, but, additionally, they adopt the shape imposed on it by surrounding structures like ribs. Medial surfaces include the hilum, the esophagus, and the trachea.. Figure 2.1: Pulmonary anatomy: a) lung lobes (source [2]), and b) airway generations (source [3]).. Trachea is a cylinder-like shape of about 2 cm in external diameter. The relaxed adult trachea extends from the sixth cervical vertebra to the carina. At that point, the trachea 4.

(10) Airway tree segmentation based on CT images in patients with (ARDS). 5. bifurcates into the right and left main-stem or the first generation of bronchi. Each division of bronchial tree is called generation. Further branching occurs to supply the individual lobes of the lungs; those bronchi conform lobar or secondary bronchi level [4]. Finally, the tertiary bronchi or segmental bronch end in lobular bronchioles, terminal bronchioles, or respiratory bronchioles, which lead into the alveolar ducts and sacs [3]. From the 17th generation, bronchi perform the gaseous exchange via the pulmonary arterial blood supply (see Fig. 2.1 b)). When a branch bifurcates into two child branches, fluid through the parent branch is also divided into two children [5]. In general, an expert can identify up to the seventh generation of bronchi on a CT image. Airways present some resistance to airflow due to their limited volume [6], affecting the optimization of lung function, which is reached aligning ventilation and pulmonary blood flow at a regional level. Diseases decrease this function because of the alteration of the lung structure, leading to a reduction of the ventilation and perfusion matching [7].. Figure 2.2: Comparison of a CT image between a) a pathologic lung and b) a normal lung.. ARDS is a clinic manifestation, characterized by diffuse lung injury that leads to respiratory failure and death. In addition, lungs present inflammation and hyperpermeability, as response to various aggressions in the parenchyma [8]. ARDS represents almost 5% of the entrances in Emergency Room in France and a mortality of 40% of people with this disease [9]. Treatment of the sickness consists in mechanical ventilation, which represents a risk by itself because it can augment or cause new lung injuries, indistinguishable from those caused by ARDS. For this reason, mechanical ventilation is the cause of 25% of deaths of the ARDS’s mortality rate. This syndrome is caused by a direct pulmonary aggression like a pneumopathy or an external injury, and evolves from the inflammation to the necrosis and fibrosis associated to the progressive loss of parenchymal membranes..

(11) Airway tree segmentation based on CT images in patients with (ARDS). 6. Fig. 2.2, shows some differences between an ARDS affected lung and a healthy one. Lung collapse can be seen in the sick lung as the decrease of the contrast between parenchyma and the surrounding tissues, as seen in red. In a normal lung, the aforementioned contrast is highly visible in the CT image..

(12) Chapter 3. State of Art In order to quantify the amount of air inside the lungs, researches in ARDS classify lung parenchyma into regions, using CT scans to analyze the entire pathologic lung. CT images use Hounsfield Units (HU) to represent intensity levels related to the different densities detected by the capturing device. In this sense, water is assigned a value of 0, air a value of -1000, and any other tissue different positive values [10]. Each voxel of a CT image is classified as non-aerated (density between +100 and -100 HU), poorly aerated (density between -101 and -500 HU), normally aerated (density between -501 and -900 HU), or hyperinflated (density between -901 and -1000 HU) [11, 12]. These ranges are widely used [13, 14] and provide a diagnosis and a tracking tool, since the reduction of the lung volume is not homogeneous and changes as the disease progresses. By keeping lungs open, physicians prevent lung collapse and injury. On the other hand, Pressure-volume curve (PV curve) is a representation of the aerated tissue in the lung during the mechanical ventilation treatment, this consists in safely increasing the positive end-expiratory pressure (PEEP) of the lung. PEEP is a method of ventilation in which pressure is maintained during the exhalation phase for increasing the volume of gas kept in the lungs at the end of expiration in order to improve the gas exchange [15]. PV curve begins with a volume of equilibrium with a PEEP of zero and ends when the PEEP reaches a volume near to the total lung capacity [16]. Fig. 3.1 shows a comparison between the PV curve of normal and ARDS lungs. In the image, T LC represents Total Lung Capacity, F RC is the Functional Residual Capacity, RV is the Residual Volume, P inf is the Lower Inflection Point and P sup is the Upper Inflection Point. PV curve has been widely used to estimate respiratory system mechanics in patients with ARDS [18, 19]. Both curves have a sigmoidal shape. Slope in ARDS patients is highly reduced in comparison to normal patients [17]. This is explained by 7.

(13) Airway tree segmentation based on CT images in patients with (ARDS). 8. Figure 3.1: Comparison of PV curve. Normal lung vs ARDS (source [17]). the fact that lung capacity is affected by the pathology. The lower inflection point represents the level of pressure at wich the lungs suddenly open. The upper inflexion point represents lungs overaeration. Airway segmentation is a fundamental step in the treatment and research of ARDS because it affects the measure of lung capacity, especially in the overaerated region. As mentioned in Chapter 2, airways have a tree-like structure where bifurcations in the tree go up the 23rd generation. Each branch has a cylindrical shape with decreasing radius [20]. So far, various methods have been proposed for segmenting the airway tree, some of them automatically and others semi-automatically. One of the most commonly used methods is the region growing algorithm [21] because it assumes no prior knowledge of the shape or size of the airways. During the region growing, it is necessary to identify of one or more seeds in the bronchial structure. Those seeds can be interactively chosen by a user [22, 23]. Seeds grow, by subsequent iterations in their neighborhood, to a determined threshold. The main difficulty of this algorithm is to find a threshold that allows segmentation of many branches avoiding any leakage to the lungs. This is possible because the airway wall has a relatively high intensity in comparison to the bronchi lumen. Some authors as [24, 25] propose a fixed threshold between -500 and -577 HU. In this sense, [26] they use thresholding to focus on specific regions that might contain bronchi. Others, as [27], state that all voxels with an intensity value below -950 HU are part of an airway, afterwards, the upper threshold is fixed in -775HU. A technique to automatically determine a threshold was proposed by [28], who sets a threshold by consecutive region growing with controlled leakage - a technique for finding the point between two.

(14) Airway tree segmentation based on CT images in patients with (ARDS). 9. consecutive thresholds where the segmented volume increases too much. Finally, other authors have found the threshold to be dependent on the bronchi size and level [29]. Nevertheless, the airway wall can present “holes” in the image due to involuntary movements of the body, partial volume effect, or noise. When these holes are present, the segmentation threshold is lower, and some branches are lost. Improvements to the threshold-based region growing techniques include leakage detection and correction [30] or a variational threshold [31–33]. Wave front propagation techniques as [34, 35] are useful for avoiding leakage and tracking the structure of the airway segmentation. They include Fast Marching algorithms [35], which are used for segmenting tubular structures in combination with other techniques as tubular enhancing filters. Fast Marching begins from a seed point and continues as a region growing method, but with the possibility of an unexpected stop of the segmentation, especially when there exists an interruption such as a tumor, fluid plugs, or intensity gaps. Further, more sophisticated methods consider a-priori knowledge of the bronchi anatomy. As mentioned before, bronchi have a tubular-like structure with decreasing radius. In [20, 36, 37] some filters for enhancing or detecting tubular structures are used. In this type of approach, luminal region of tubular structures such as vessels or bronchi are detected by performing a Hessian matrix analysis for obtaining a medialness map [37]. Depending on the analysis of the eigenvalues of the Hessian matrix, segmentation can go further crossing over disconnections minimizing leakage. Table 3.1 shows general interpretation of the eigenvalues λ of the Hessian matrix [38]. λ1 N L L L L HH+. λ2 N L L HH+ HH+. λ3 N HH+ HH+ HH+. Orientation pattern Noisy, no preferred direction plate-like structure (bright) plate-like structure (dark) tubular structure (bright) tubular structure (dark) blob-like structure (bright) blob-like structure (dark). Table 3.1: Possible patterns in 3D, depending on the value of the eigenvalues λk (H=high, L=low, N=noisy, usually small, +/- indicate the sign of the eigenvalue). The eigenvalues are ordered: | λ1 | ≤ | λ2 | ≤ | λ3 |. Nonetheless, ADRS causes a condition called “sponge lung”, corresponding to the alveolar collapse and the inflammatory reaction that makes some parenchyma regions to look tubular..

(15) Airway tree segmentation based on CT images in patients with (ARDS). 10. It is well known that airways are accompanied by arteries, and this feature has been used in both airway and vessel segmentation [34, 39, 40]. Vessels are especially useful for airway segmentation in CT because of their better visibility, as shown in [39], e.g. [34] uses vessel direction for improving bronchi detection. However, these methods usually assume that the vessels are enhanced by a contrast agent, which is not used in patients with ARDS. In these patients, vessel detection is a challenge by itself. Some techniques are based on directional region of interest [41–43]. In these techniques, the algorithm takes a directional region (D-ROI) around a point. It is estimated as the center of the branch in a plane orthogonal to the branch’s axis. The D-ROI can be implemented by several shapes, for example [41, 42] uses a cylinder, whereas [43] uses a box..

(16) Chapter 4. Method The dataset used for the study, is composed by CT images from 8 pigs with induced ARDS. Table 4.1 shows general information about the dataset. Each animal was treated with mechanical ventilation, in which experts increased first the volume and after the PEEP up to a safe volume. A CT scan was taken at the end of aspiratory and expiratory phases. Each volume and PEEP combination is a condition of aeration. There were 20 different conditions, resulting in a total of 40 datasets per pig. Images were reconstructed with a spacing of 1 and 5mm. The dimensions of the images with spacing of 1mm were of 512 × 512 × 370, and the voxel size of 0.5 × 0.5 × 1 mm3 , whereas in images of 5 mm the respective values were of 512 × 512 × 59 and 0.5 × 0.5 × 5 mm3 . Table 4.1: General information of the 8 pigs for testing. Pig01 Pig02 Pig03 Pig04 Pig15 Pig16 Pig 17 Pig18 Pig19. Series Name COCGE04163 COCGE04224 COCGE04226 COCGE04235 COCGE04443 COCGE04456 COCGE04511 COCGE04603 COCGE04610. Thickness 1mm and 5mm 1mm and 5mm 1mm and 5mm 1mm and 5mm 1mm and 5mm 1mm and 5mm 1mm and 5mm 1mm and 5mm 1mm and 5mm. The project was carried out in three stages. In the first one, an initial lung segmentation was obtained. This first segmentation was used as a mask to limit the area for the next two phases. In the second stage, two complementary algorithms were developed to retrieve airways. In the third stage, information from stage one and two was used to quantify each lung region. 11.

(17) Airway tree segmentation based on CT images in patients with (ARDS). 12. Theory underlying each phase’s mechanism is now considered.. 4.1. Lung Parenchyma Segmentation. ARDS treatment requires calculation of lung volume. This is a difficult task to perform automatically still a fundamental step for the research. In this sense, an interactive segmentation tool for 3D medical images called TurtleSeg [44] was used. This tool allows specialists generate a volume of an enclosed region by tracing 2D contours on various slices of the CT image in different orthogonal planes, as seen in Fig. 4.1. Figure 4.1: Lung Segmentation usingTurtleSeg.. The tool helps users in the 2D contour tracing and in the plane selection for contour generation, using a method called ”Livewire”[45]..

(18) Airway tree segmentation based on CT images in patients with (ARDS). 4.2. 13. Airway Segmentation. Within the project, two complementary methods were implemented for airway segmentation. The first method consisted in a region growing based on gray levels of the CT image in which the user defined a seed point in the trachea or in any other part inside the airway tree, and the nearest neighboring pixels were examined to determine if they had to be added to the seeds, as explained in [21]. Initially, it was used an approach of controlled volume explosion [28] for threshold determination, consisting in performing several segmentations, increasing the upper threshold to find the intensity level at which a volume explosion occurs. Curves of threshold vs segmented volume were used to evaluate data for this method, allowing improvement in the execution time of the algorithm. Finally, the optimal threshold was found by a binary search between the air intensity (-1000Hu) and the highest possible value for airway segmentation (-500HU). If segmentation fails, there exists the option to find a manual threshold for a satisfactory segmentation. As the simple region growing may miss some branches, the algorithm was complemented by a Variational Region Growing (VRG) to go farther in the segmentation and over the gaps. VRG is a segmentation method that evolves not just by gray level, but by minimization of an energy function J [46]. Let φn be the evolving region at iteration n, defined by eq. 4.1: ( φn (x) = 1 φn (x). if x Ωin. = 0 if x Ωout. (4.1). Where Ω is the image domain and Ωin is a region inside Ω, and Ωout = Ω \ Ωin . φ varies according to the energy criterion J, which is region-dependent. At iteration n + 1 [46], J φn+1 is shown in eq. 4.2:. J φn+1 = J (φn ) + ∆J φn+1. (4.2). Pacureanu [47], used Chan and Vese’s model [48] of region active contour, and proposed a segmentation method using VRG adapted for lacuno-canalicular networks introducing shape’s prior information. This work introduces a measure of vesselness and defines energy variation as in eq. 4.3:. ∆J φn+1 = (1 − 2φn ) [∆J1 (v) + ∆J2 (f, v)]. (4.3).

(19) Airway tree segmentation based on CT images in patients with (ARDS). 14. where h i ∆J1 (v) = v |v − µvin |2 − |v − µvout |2. (4.4). h i ∆J2 (f, v) = (1 − v) |f − µf in |2 − |f − µf out |2. (4.5). and. v is the vesselness measure obtained by Frangi [38], f is the gray intensity of the point to be evaluated, µvin , µvout the mean of the internal and external region in the vesselness image respectively, and µf in , µf out the mean gray levels of the internal and external region. For detecting dark tubular structures using vesselness measure of Frangi [38], it was used the following approach:.   0 if λ2 < 0 or λ3 < 0 2 2 v (x) = 2 RB RA S  1 − exp − 2 exp 1 − exp − 2 otherwise 2 2a. 2b. (4.6). 2c. where |λ1 | |λ1 | RA = , RB = p and S = λ3 |λ2 λ3 |. q λ21 + λ22 + λ23. (4.7). a, b, and c are constant parameters, and RA , RB , and S generate a measure, where the highest value corresponds to the best response of the filter. Each λ represents the eigenvalues of the Hessian matrix, shown in Table 3.1. Global analysis of the airway tree using a medialness-vesselness measure is time consuming plus the amount of errors can be high because some lung areas outside of the bronchial structures may present a high measure. This is why the first region growing segmentation is used as seed to perform a VRG on initial tree’s endings (eq. 4.3). At this point, a Volume of Interest(VOI) is defined and the endings are enclosed within it as seen in Fig. 4.2. The selection of the scale of the analysis σ depends on the size of the branch inside the VOI, and goes from the calculated diameter of the branch, σmax , to σmax /4. These modifications help to obtain a better vesselness response in the dark tubular structures computed in eq. 4.4, but it is necessary to complement them with image intensity.

(20) Airway tree segmentation based on CT images in patients with (ARDS). 15. Figure 4.2: Algorithm used for bronchi segmentation. a) Initial region growing segmentation. b) Finding tree’s endings in the initial segmentation. c)Defining VOI’s for enclosing initial segmentation endings. d) Results of the VRG segmentation algorithm inside the VOIs. information. Eq. 4.5 is useful for segmenting the ellipsoidal shaped structures, which are not sufficiently enhanced by the vesselness algorithm. This information helps propose an approach for using intensity information in dark tubular structures. A modification to eq. 4.3 is proposed as follows:. ∆J φn+1 = (1 − 2φn ) [∆J1 (v) + ∆J2 (f )]. (4.8). h i ∆J1 (v) = v/2 |v − µvin |2 − |v − µvout |2. (4.9). where.

(21) Airway tree segmentation based on CT images in patients with (ARDS). 16. and. h i ∆J2 (f ) = f /2 |f − µf in |2 − |f − µf out |2. (4.10). In eq. 4.9, values of v, µvin and µvout are normalized by the highest response of the vesselness filter in the VOI, and values of eq. 4.10 f , µf in , µf out are normalized by the highest intensity value in the lung region. Maximum threshold in gray intensity level and a minimum response of the vesselness measure of the analyzed point were included for minimizing leakage.. 4.3. Aeration Levels and PV Curves. As explained in Chapter 3, voxels can be grouped into zones in the CT images depending on the intensity level. Taking into account this, lung volume was classified and assigned a colour for each zone.. Figure 4.3: Color map for each zone in the lung region. Fig. 4.3 provides information about how the different lung regions were classified according to their intensity levels. Previous researches show correspondence between air proportion and HU value for a voxel [49]. %air = 1 −. HU − HUair HUwater − HUair. (4.11). The approach in eq. 4.11 refers to the percentage of air on a voxel in a CT image. This is defined as the difference between the intensity level of the voxel (HU ) and the air level (HUair ), in proportion to the range of values that can contain air, meaning,.

(22) Airway tree segmentation based on CT images in patients with (ARDS). 17. the difference between the water reference level (HUwater ) and the air reference level (HUair ). PV curves were generated for each lung volume; such curves should have a sigmoidal shape [50], defined by eq. 4.12 ". b V =a+ 1 + exp − P d−c. # (4.12). where V is inflation or absolute lung volume, P is airway opening or transpulmonary pressure, a corresponds to the lower asymptote volume, b to the total change in volume between the lower and upper asymptotes, c is the pressure at the inflection point of the sigmoidal curve, and d is proportional to the pressure range within which most of the volume change takes place (see Fig. 4.4).. Figure 4.4: Representation of the principal points of the PV curve. First, absolute volume of the region was used to generate the PV curves, and then, eq. 4.11 was applied for attaining accurate measures, thus, enhancing the curves. The results were visually compared with those from previous researches performed over humans [51]..

(23) Chapter 5. Results and Discussion 5.1. Lung Parenchyma Segmentation. By the time of this project, it is not possible to estimate the accuracy of the proposed segmentation because we do not have a ground truth reference. However, qualitatively (see Fig. 5.1), the results seems to be usable.. Figure 5.1: Lung Segmentation usingTurtleSeg.. The visual assessment shows a good segmentation in most of the slices, but there are some of them that seems incomplete. Fig. 5.2, shows segmented lung mask in bright white, overlaying the original slice. The red circle shows a lung area that was not included in the mask. 18.

(24) Airway tree segmentation based on CT images in patients with (ARDS). 19. Figure 5.2: Lung Segmentation using TurtleSeg. Some pixels missing.. A rough counting of missing pixels gives an amount of up to 1% of voxels, belonging to lungs that were not segmented (false negative), see Table. 5.1 Table 5.1: Manual count of missing lung voxels in mask. Conditions P=18, Expiration P=10, Expiration P=18, Inspiration P=10, Inspiration. Missing 39804 33853 63722 50722. Existing 10627063 8325312 11497348 9346385. Proportion 0.004 0.004 0.006 0.005. This proportion is different among segmentations and was obtained by visually labeling the missing voxels in the segmented volumes, using Manual paint, a tool of CreaTools. Despite this not being a representative sample, it allowed to implement a validation method where each original mask was complemented with a region growing process in the lung. Every volume of Pig01 was analyzed, and a maximum difference of 1.5% was found. False positives rate is more difficult to calculate without any references. Segmented volumes will be kept, and they could serve as a comparison point when a better lung segmentation is obtained..

(25) Airway tree segmentation based on CT images in patients with (ARDS). 5.2. 20. Airway Segmentation. As mentioned in Chapter 4, the airway tree was segmented using a controlled explosion approach. Data was evaluated in the range between -1200HU and -500HU, for CT images of four pigs with spacing of 1mm. In this sense, some curves of threshold vs amount of segmented voxels were generated (see Fig. 5.3).. Figure 5.3: Curves of threshold (HU) vs amount of segmented voxels for three pigs each at a certain condition of aeration.. The curve for Pig01, corresponds to conditions of Volume = 10 and PEEP = 5; for Pig02 PEEP = 12 and Volume = 6; and for Pig03, PEEP = 12 and Volume = 6, where.

(26) Airway tree segmentation based on CT images in patients with (ARDS). 21. volume is the proportion of the amount of air per kilo (weight), and PEEP is in units of cmH2 O. Fig. 5.3 shows a rising trend and a constant slope in the curves generated. This trend is suddenly disrupted by a volume explosion, visualized as a big change in the amount of segmented voxels and represents bronchi leakage to the lungs. At this point the segmentation must stop. Although samples for each pig were taken under similar conditions, the threshold where volume explosion occurs is different. The airway tree for each pig is shown in Fig. 5.4.. Figure 5.4: Airway masks representation. a) Pig01, b) Pig02, c) Pig03. Sometimes the curves present two explosions as in Fig. 5.5. In this case, depending on which explosion the algorithm finds first, segmentation may present a leakage problem. In order to avoid this problem, the implemented algorithm always looks for the first explosion.. Figure 5.5: a) Curve of the amount of segmented voxels vs. threshold (HU) . b) Airway segmentation with leakage..

(27) Airway tree segmentation based on CT images in patients with (ARDS). 22. Segmentation results are used as seeds for the VRG algorithm presented in Chapter 4; this technique ensures the final results to have at least the same amount of branches as the threshold-based region growing. Table 5.2 compares the gray level region growing segmentation against the VRG for datasets of Pig01. The table shows total amount of segmented voxels for bronchi and the amount of new voxels segmented by the novel algorithm. Table 5.2: Bronchi segmentation results for Pig01. Conditions P= 10, V= 5 P=15, V=5 P=4, V=5 P=5, V=5 P=6, V=5 P=8, V=5 P=6, V=10 P=6, V=12 P=6, V=14 P=6, V=16 P=6, V=18 P=6, V=2 N/A , V=40 P=6, V=4 P=6, V=6 P=6, V=8 P=10,V=0 P=6, V=20. Expiration Total Total New 72245 4598 72098 4151 54886 729 58785 1496 57636 814 65722 2378 75318 4982 82984 5671 85721 7955 91317 12020 98308 14202 48880 991 121977 11176 48345 1319 63946 1579 68808 2195 39570 750 107200 7287. Proportion 0.06 0.06 0.01 0.03 0.01 0.04 0.07 0.07 0.09 0.13 0.14 0.02 0.09 0.03 0.02 0.03 0.02 0.07. Inspiration Total Total New 92732 8360 114903 15009 69423 2058 72574 2841 84344 5500 88913 7783 92258 6484 98351 13401 120491 13048 106426 7594 102973 10300 111764 12816 79512 6276 77260 3922 88692 6108 89449 7892 67950 3150 110510 8930. Proportion 0.09 0.13 0.03 0.04 0.07 0.09 0.07 0.14 0.11 0.07 0.10 0.11 0.08 0.05 0.07 0.09 0.05 0.08. As seen in Table 5.2, the proportion of additional voxels does not exceed 14% of the initial segmentation. The final volume is shown in Fig. 5.6. New pixels are shown in dark green. Grey level region growing goes up to bronchi generation 3 or 4. The VRG method goes to generation 4 or 5, but favoring new branches at the end of existing ones. Ground truth is not known for a complete validation.. 5.3. Aeration Levels and PV Curves. PV curves were generated by counting the voxels in the CT image, using the air proportion approach (eq. 4.11), as mentioned in Chapter 4..

(28) Airway tree segmentation based on CT images in patients with (ARDS). 23. Figure 5.6: Segmentation results with the novel algorithm. Figure 5.7: PV curve for Pig01. Initial counting of all voxels. Those curves were generated for Pig01, Pig15, and Pig16 using a lung mask of 1mm of spacing. Fig. 5.7, shows the relation between pressure and volume for Pig01. The same curve was generated for Pig17 and Pig18 with a lung mask of 5mm of spacing and after a linear interpolation for generating a volume of a voxel thickness of 1mm. Fig. 5.8 shows the Result for Pig17. Curves in Fig. 5.7 and 5.8 can be approximated through eq. 4.12, but error in the generated curve for Pig17 seems to be higher due to the introduction of a new variable when interpolating. For Pig01 goodness of fit of the inspiration process is R2 = 0.9989 and for expiration process is 0.9912, while for Pig17 R2 = 0.906 for expiration and 0.894 for inspiration. Visually, PV curves for Pig17 have more discontinuities. Such curves could be a step for evaluating future segmentation models. This validation could be done in the case of Pig01 and Pig17, but in other cases, parameter information of eq. could not be retrieved. Sometimes, values for a, d, b or c are not clear because the curve.

(29) Airway tree segmentation based on CT images in patients with (ARDS). 24. Figure 5.8: PV curve for Pig 17. Initial counting of all voxels. is not complete. To complete the curve, it is necessary to take more samples, including when lungs are totally deflated and when completely full.. 5.3.1. Over-aerated Class. For Pig01, the lung amount of voxels in class vs. PEEP is shown in Fig. 5.9, and the bronchi’s in Fig. 5.10. Figure 5.9: Over-aerated class Pig 01. Initial counting of all voxels in the overaerated region. Both graphs are similar; this means that most of the voxels of the overaerated class correspond to bronchi. Total proportion of bronchi over this class does not exceed 2% of lung volume, as seen in Table 5.3.

(30) Airway tree segmentation based on CT images in patients with (ARDS). 25. Figure 5.10: Overaeratedcclass Pig01. Bronchi initial counting of all voxels in the over-aerated region for bronchi Table 5.3: Proportion bronchi over-aerated class over total lung volume. PEEP 20 18 16 14 12 10 8 6 5 4 2. Expiration 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01. Inspiration 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01. As seen in Table 5.4, the contribution of the new algorithm to the overaerated class segmentation is not high because most of the new segmented pixels are in the normallyaerated class.. 5.3.2. Normal-aerated class. The amount of voxels in class vs. PEEP is shown in Fig. 5.11. It is important to mention that proportion of air in voxels included in the bronchi segmentation for this class does not exceed 1.5% as shown in Table 5.5 The bronchi proportion in this class does not greatly affect the air volume measurement within the lung, hence, specialist rather use the overaerated class for bronchi analysis.

(31) Airway tree segmentation based on CT images in patients with (ARDS) Table 5.4: Proportion of new bronchi normally-aerated class to total bronchi segmentation. PEEP 2 4 5 6 8 10 12 14 16 18 20. Expiration 0.000 0.000 0.000 0.000 0.003 0.001 0.000 0.003 0.005 0.002 0.001. Inspiration 0.000 0.001 0.000 0.001 0.003 0.000 0.001 0.003 0.001 0.002 0.000. Figure 5.11: Normally-aerated class Pig01. Bronchi initial counting of all voxels in the normally-aerated region Table 5.5: Proportion of bronchi normally-aerated class to total lung volume. PEEP 20 18 16 14 12 10 8 6 5 4 2. Expiration 0.0158885378 0.0110947468 0.0118355658 0.011337004 0.0085921084 0.0066237131 0.0064859852 0.0051704382 0.0050557797 0.0052459505 0.0057593294. Inspiration 0.0139407797 0.0099416647 0.0101992304 0.0103267002 0.008225744 0.0074773185 0.0086603761 0.0060315226 0.0048471027 0.0051458856 0.0048721356. 26.

(32) Chapter 6. Conclusions A study for impact analysis of air volume in bronchi in CT images, including a tool for segmenting airways and measuring different zones in lungs with ARDS was presented. In the process, a simple approach for improvement of a previous generated lung mask was implemented, but showed the need for a better implementation in order to get enhanced results. This restriction suggests a path for future work. However, this was not a limitation to continue the project. Data from 37 CT images, each at different aeration conditions for Pig01, showed that the proportion of missing voxels did not exceed 1.5%. Despite not being able to measure the false positive rate in the lung segmentation, the airway segmentation analysis was not significantly affected because the lung segmentation covered almost totality of bronchi area. Data will be kept for future analysis. Regarding bronchi segmentation, two algorithms were presented. The first one, a leakage controlled approach reached most of the detected bronchi, and was used in the initialization of the second algorithm for going farther. This algorithm goes to the 4 or 5 generation of airways, and segments up to 14% more of bronchi in pathologic lungs, depending on the aeration conditions. It was not easy to compare with other algorithms as it does not exist a segmentation reference for pathologic lungs. It is necessary to mention that ARDS changes the parenchyma aspect, and it is easy to leak into the lung when segmenting airways. Finally, PV curves proved to be a good tool for data validation. It was evident that for overaerated class and normally-aerated class, air volume in the inspiration process was higher than in the expiration process, and augmented with pressure. The bronchi segmentation in the overaerated class occupied most of the volume of this class, and did not exceed 2% of lung area in the pigs. Future work could include a better technique for evaluating airway segmentation and branch counting. Additionally, the proposed method could be improved in order to 27.

(33) Airway tree segmentation based on CT images in patients with (ARDS). 28. include all the voxels segmented by region growing, leading to more reliable results when using VRG for airway segmentation..

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