Automatic tumour segmentation in H&E-stained whole-slide images of the pancreas. (arXiv:2112.01533v1 [eess.IV])

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. 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 $mu$m. Our
multi-task network improved on that with a median Dice score of 0.934 (0.077)



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