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

PeerJ

article

Péter Bándi¹  Maschenka Balkenhol¹  Bram van Ginneken²  Jeroen van der Laak¹  Geert Litjens¹ 

[1] Department of Pathology, Radboud University Medical Center;Department of Radiology and Nuclear Medicine, Radboud University Medical Center

关键词: Whole-slide images;    Convolutional neural networks;    Segmentation;    Tissue;    Deep learning;    Computational pathology;   

DOI  :  10.7717/peerj.8242

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

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.

【 授权许可】

CC BY