Comparison of Different Methods for Tissue Segmentation in Histopathological Whole-Slide Images

Tissue segmentation is an important pre-requisite for efficient and accurate diagnostics in digital pathology. However, it is well known that whole-slide scanners can fail in detecting all tissue regions, for example due to the tissue type, or due to weak staining because their tissue detection algorithms are not robust enough. In this paper, we introduce two different convolutional neural network architectures for whole slide image segmentation to accurately identify the tissue sections. We also compare the algorithms to a published traditional method. We collected 54 whole slide images with differing stains and tissue types from three laboratories to validate our algorithms. We show that while the two methods do not differ significantly they outperform their traditional counterpart (Jaccard index of 0.937 and 0.929 vs. 0.870, p < 0.01).Comment: Accepted for poster presentation at the IEEE International Symposium on Biomedical Imaging (ISBI) 201

Adaptive Localization of Focus Point Regions via Random Patch Probabilistic Density from Whole-Slide, Ki-67-Stained Brain Tumor Tissue

Analysis of whole-slide tissue for digital pathology images has been clinically approved to provide a second opinion to pathologists. Localization of focus points from Ki-67-stained histopathology whole-slide tissue microscopic images is considered the first step in the process of proliferation rate estimation. Pathologists use eye pooling or eagle-view techniques to localize the highly stained cell-concentrated regions from the whole slide under microscope, which is called focus-point regions. This procedure leads to a high variety of interpersonal observations and time consuming, tedious work and causes inaccurate findings. The localization of focus-point regions can be addressed as a clustering problem. This paper aims to automate the localization of focus-point regions from whole-slide images using the random patch probabilistic density method. Unlike other clustering methods, random patch probabilistic density method can adaptively localize focus-point regions without predetermining the number of clusters. The proposed method was compared with the k-means and fuzzy c-means clustering methods. Our proposed method achieves a good performance, when the results were evaluated by three expert pathologists. The proposed method achieves an average false-positive rate of 0.84% for the focus-point region localization error. Moreover, regarding RPPD used to localize tissue from whole-slide images, 228 whole-slide images have been tested; 97.3% localization accuracy was achieved

Training based segmentation for tissue extraction in whole slide image

Reducing the time and storage memory required for scanning whole slide images (WSIs) is crucial. In this thesis work we tested and assessed the performance of two popular neural network architectures, namely DeepLabV3+ and Unet. In addition to that, a desktop application used to annotate histopathology images was developed, such application ultimately provided the data needed in order to train the neural networks. Both DeepLabV3+ and Unet accurately separated the regions of interest out of the WSIs, however DeepLabV3+ outperformed Unet, striking a pixel wise accuracy of 96.3%, while Unet scored 94.7% in the same metric. Morover DeepLabV3+ also outscored Unet in the IoU metric with values of 0:446 and 0:398 respectively. We showed the effectiveness of using deep neural networks for the case of semantic segmentation in histopathology images, more specifically for extracting tissue areas from WSIs, and how this can be used to improve the performance of WSI scanners