Automatic tumour segmentation in H&E-stained whole-slide images of the pancreas.

P. Vendittelli; E. M. M. Smeets; G. J. S. Litjens

doi:10.1117/12.2611542

4 April 2022 Automatic tumour segmentation in H&E-stained whole-slide images of the pancreas.

P. Vendittelli, E. M. M. Smeets, G. J. S. Litjens

Proceedings Volume 12039, Medical Imaging 2022: Digital and Computational Pathology; 120391F (2022) https://doi.org/10.1117/12.2611542
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

Pancreatic cancer will soon be the second leading cause of cancer-related death in Western society. Imaging techniques such as CT, MRI and ultrasound typically help providing the initial diagnosis, but histopathological assessment is still the gold standard for final confirmation of disease presence and prognosis. In recent years machine learning approaches and pathomics pipelines have shown potential in improving diagnostics and prognostics in other cancerous entities, such as breast and prostate cancer. A crucial first step in these pipelines is typically identification and segmentation of the tumour area. Ideally this step is done automatically to prevent time consuming manual annotation. In this paper, we propose a multi-task convolutional neural network to balance disease detection and segmentation accuracy. We validated our approach on a dataset of 29 patients (for a total of 58 slides) at different resolutions. The best single task segmentation network achieved a median Dice of 0.885 (0.122) IQR at a resolution of 15.56 μm. Our multi-task network improved on that with a median Dice score of 0.934 (0.077) IQR.

Conference Presentation

Citation Download Citation

P. Vendittelli, E. M. M. Smeets, and G. J. S. Litjens "Automatic tumour segmentation in H&E-stained whole-slide images of the pancreas.", Proc. SPIE 12039, Medical Imaging 2022: Digital and Computational Pathology, 120391F (4 April 2022); https://doi.org/10.1117/12.2611542

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