Resolution-agnostic tissue segmentation in whole-slide histopathology images with convolutional neural networks

Abstract

Modern pathology diagnostics is being driven toward large scale digitization of microscopic tissue sections. A prerequisite for its safe implementation is the guarantee that all tissue present on a glass slide can also be found back in the digital image. Whole-slide scanners perform a tissue segmentation in a low resolution overview image to prevent inefficient high-resolution scanning of empty background areas. However, currently applied algorithms can fail in detecting all tissue regions.In this study, we developed convolutional neural networks to distinguish tissue from background. We collected 100 whole-slide images of 10 tissue samples—staining categories from five medical centers for development and testing. Additionally, eight more images of eight unfamiliar categories were collected for testing only. We compared our fully-convolutional neural networks to three traditional methods on a range of resolution levels using Dice score and sensitivity.We also tested whether a single neural network can perform equivalently to multiple networks, each specialized in a single resolution. Overall, our solutions outperformed the traditional methods on all the tested resolutions. The resolution-agnostic network achieved average Dice scores between 0.97 and 0.98 across the tested resolution levels, only 0.0069 less than the resolution-specific networks. Finally, its excellent generalization performance was demonstrated by achieving averages of 0.98 Dice score and 0.97 sensitivity on the eight unfamiliar images. A future study should test this network prospectively.

FastPathology: An Open-Source Platform for Deep Learning-Based Research and Decision Support in Digital Pathology

“…Thus, if suboptimal tissue segmentation on a specific WSI is observed, the threshold can be manually adjusted by the user in FastPathology. Similar behaviour of Otsu's method has been observed in related work by Bandi et al [42]. They proposed a more advanced approach using deep learning.…”

Deep Learning-Based Identification of Spinal Metastasis in Lung Cancer Using Spectral CT Images

“…General image segmentation algorithms mostly use the pooling layer and the convolutional layer to zoom in the receptive field and zoom out the feature map and then restore the image through upsampling. e image will be damaged during the process of zooming in and zooming out, while the DC avoids image loss by replacing the upsampling and downsampling processes [9]. e study aims to improve the diagnostic efficiency of energy/spectral CT images for spinal metastasis from lung cancer.…”

Developing image analysis pipelines of whole-slide images: Pre- and post-processing

“…The use of thresholds provides a very quick method to identify most artifacts, but not all. One elegant alternative is to use a deep learning segmentation algorithm to specifically discriminate background or noise (such as dust particles or pen markings) from foreground (tissue) [22].…”

“…Doing so may limit the generalizability of trained models to real-world datasets and clinical applications. Nevertheless, sampling in this way under samples slide background, sparing computational expense, and can be used in multistep analysis pipelines where an initial model is used to identify large-scale ROIs followed by analysis within those regions [22,32].…”