Table 1.
Overview of the four different dataset used in this study.
Fig 1.
Example of dataset A (first row H&E stained WSI, corresponding CK18/8 stained WSI on the second row).
The staining-restaining procedure results in perfectly matching slides.
Fig 2.
Flowchart highlighting different pipeline steps: (a) Epithelium segmentation and (b) tumor epithelium segmentation. Through the process of staining-destaining of paired H&E and IHC slides, epithelium annotations are obtained, which are then used to train an epithelium segmentation network (dataset A). This network annotates the rest of the slides. Subsequently, a tumor epithelium segmentation network is trained on the segmented epithelium combined with annotated tumor area (dataset B). Based on tumor epithelium segmentation, the tumor bulk is automatically determined by drawing a convex hull (c), on which TSR is calculated. Legend: In green the epithelium segmentation network, in yellow the tumor epithelium segmentation network and in red the resulting tumor bulk segmentation and TSR quantification.
Table 2.
Results of the various tasks.
Fig 3.
Example of epithelial segmentation network on two different slides from dataset A.
Left column: example of two patches extracted from two different cases. Middle column: in green the overlay of the epithelium extracted from the CK18/8 stain. Right column: output of the network (in green the segmented epithelium). As we can see, lymphoid aggregates are correctly recognised as non-epithelium from the network.
Fig 4.
Example of tumor epithelial segmentation network on a slide from dataset B.
First column: a randomly extracted patch from the dataset. Middle column: ground truth obtained by inferring the Epithelium segmentation network (top left cluster of epithelial cells are non cancerous, bottom right are cancerous). Right column: output of the tumor epithelium network. As we see, tumor epithelium is correctly segmented while healthy epithelium is discarded.
Fig 5.
Example of the performance of the alphahull algorithm on dataset D.
Middle column is the coarse tumor annotation made by pathologist, while right column is the convex hull automatically generated on the segmented tumor epithelium.
Fig 6.
AUC of the Logistic Regression model across five-fold cross-validation.
Shade in the area represents the standard deviation across all folds. The figure reports three experiments, 6 months, 12 months, and 18 months survival, respectively.
Fig 7.
Stratification was done using the mean TSR value.