Actividad Emprendedora Temprana (TEA) a nivel mundial

We use 10 hand-marked images in the evaluation of the proposed localisation method. We are unable to numerically quantify the localisation results produced by VIS, since it does not provide a functionality to output their results of luminal epithelium edge segmentation, however the results of the proposed method and VIS are visually comparable (see Figure 4.12).

We align the curve ˆC approximated by the proposed localisation method

and a hand-marked (Ground Truth) curveCGT along the x axis (horizontally). To

numerically quantify the accuracy of the proposed localisation method, Root Mean Square Error (RMSE) of the Euclidean distance in between the pixel locations of

curves ˆC and CGT in they axis (vertically) calculated as follows,

RSM E = v u u t 1 N N X i=1 (ˆy−y)2 _(4.11)

(a)

(b)

Figure 4.11: (a) Another cropped local region of the same input image showing in green Ground Truth of UNK cell nuclei, and in red the UNK cell nucleus regions detected by the proposed method. (b) The UNK cell nucleus regions (in blue) and the stromal cell nucleus regions (in green) segmented by VIS in the cropped local region of (a); note the over-segmentation of UNK cells.

where ˆyi is they coordinate of theith pixel of curve ˆC,yi is they coordinate of the

ith pixel of curve CGT and N is the total number of pixels in the curve CGT.

For the proposed localisation method, the curve describing the location of the luminal epithelium edge is approximated using 20 smaller B-Spline curves. The

average RMSE of curve ˆC for the 10 ground truth images is 23.17 pixels (10.77µm)

which can also be thought of as the average Euclidean distance between the pixel

locations of curves ˆC and CGT. The visual results produced by the proposed lo-

calisation method and VIS on three sample images are shown from Figures 4.12 to 4.14.

VIS uses a segmentation based approach to mark luminal epithelium edges. Figure 4.12 shows an example of a good segmentation result produced by VIS, however, it fails to correctly segment luminal epithelium edges in a dense nuclei environment, e.g., in Figure 4.13 (b), where many nuclei in the interior area of the tissue region are being segmented as luminal epithelium edge. In contrast, the proposed localisation method still performs well in such cases (see Figure 4.13 (a)). Moreover, VIS sometimes is unable to segment a whole luminal epithelium edge, e.g., the left part of the luminal epithelium edge in Figure 4.14 (b), while the proposed localisation method is able to detect complete luminal epithelium edge (see Figure 4.14 (a).

It is also worth noting here that the proposed localisation method is much faster, in terms of running time, compared with VIS. The proposed localisation method (currently written in MATLAB) takes on average a few minutes (on an

Intel i5 processer with 8G RAM) to process an image of the resolution of 1,000×

1,500 pixels, while VIS takes at least 6 minutes even using cloud computing. The

computational speed of the proposed localisation algorithms can be significantly improved by optimising the code in C/C++ and running on a high-end computer.

(a)

(b)

Figure 4.12: (a) shows the luminal epithelium localisation produced by the proposed method and (b) shows the luminal epithelium segmentation produced by VIS on same input images. In (a), Ground Truth of the luminal epithelium edges is depicted in green, and the luminal epithelium edge detected by the proposed method is depicted in red. In (b), the luminal epithelium segmentation produced by VIS is marked by a black bounding box.

(a)

(b)

Figure 4.13: (a) shows the luminal epithelium localisation produced by the proposed method and (b) shows the luminal epithelium segmentation produced by VIS on same input images. In (a), Ground Truth of the luminal epithelium edges is depicted in green, and the luminal epithelium edge detected by the proposed method is depicted in red. In (b), the luminal epithelium segmentation produced by VIS is marked by a black bounding box.

(a)

(b)

Figure 4.14: (a) shows the luminal epithelium localisation produced by the proposed method and (b) shows the luminal epithelium segmentation produced by VIS on same input images. In (a), Ground Truth of the luminal epithelium edges is depicted in green, and the luminal epithelium edge detected by the proposed method is depicted in red. In (b), the luminal epithelium segmentation produced by VIS is marked by a black bounding box.

4.6

Summary

In this chapter, we proposed a complete solution to effectively detect stromal and UNK cell nuclei and localising luminal epithelium edge in endometrial histology im- ages. A locality sensitive model is integrated into a cell detection method called LIPSyM, which greatly improved its performance in our cases. In addition, a novel background removal method was proposed for segmenting DAB stained brown re- gions (indicated the UNK cell nucleus regions). We also proposed a novel luminal epithelium localisation method, which can localise the luminal epithelium edges by fitting cubic B-spline curves on the epithelial cell nucleus detections identified using an alpha-shape based method. The experimental results show that the proposed

solution attains a high accuracy (F1 score: 0.84) on processing the high resolution

(1,000×1,500 pixel) local regions from the HPFs of whole slide endometrial his-

tology images. The results produced by the proposed solution were validated and approved by the collaborators.