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MILD-Net : Minimal information loss dilated network for gland instance segmentation in colon histology images
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Graham, Simon, Chen, Hao, Gamper, Jevgenij, Dou, Qi, Heng, Pheng-Ann, Snead, David, Tsang, Yee Wah and Rajpoot, Nasir M. (Nasir Mahmood) (2019) MILD-Net : Minimal information loss dilated network for gland instance segmentation in colon histology images. Medical Image Analysis, 52 . pp. 199-211. doi:10.1016/j.media.2018.12.001
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WRAP-MILD-Net-minimal-information-loss-dilated-histology-images-Rajpoot-2018.pdf - Accepted Version - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (28Mb) | Preview |
Official URL: http://dx.doi.org/10.1016/j.media.2018.12.001
Abstract
The analysis of glandular morphology within colon histopathology images is an important step in determining the grade of colon cancer. Despite the importance of this task, manual segmentation is laborious, time-consuming and can suffer from subjectivity among pathologists. The rise of computational pathology has led to the development of automated methods for gland segmentation that aim to overcome the challenges of manual segmentation. However, this task is non-trivial due to the large variability in glandular appearance and the difficulty in differentiating between certain glandular and non-glandular histological structures. Furthermore, a measure of uncertainty is essential for diagnostic decision making. To address these challenges, we propose a fully convolutional neural network that counters the loss of information caused by max-pooling by re-introducing the original image at multiple points within the network. We also use atrous spatial pyramid pooling with varying dilation rates for preserving the resolution and multi-level aggregation. To incorporate uncertainty, we introduce random transformations during test time for an enhanced segmentation result that simultaneously generates an uncertainty map, highlighting areas of ambiguity. We show that this map can be used to define a metric for disregarding predictions with high uncertainty. The proposed network achieves state-of-the-art performance on the GlaS challenge dataset and on a second independent colorectal adenocarcinoma dataset. In addition, we perform gland instance segmentation on whole-slide images from two further datasets to highlight the generalisability of our method. As an extension, we introduce MILD-Net for simultaneous gland and lumen segmentation, to increase the diagnostic power of the network.
| Item Type: | Journal Article | ||||||||||||
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| Subjects: | R Medicine > RB Pathology R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer) |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science Faculty of Science, Engineering and Medicine > Science > Mathematics |
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| Library of Congress Subject Headings (LCSH): | Colon (Anatomy) -- Cancer -- Diagnosis, Rectum -- Cancer -- Diagnosis, Adenocarcinoma, Histology, Pathological | ||||||||||||
| Journal or Publication Title: | Medical Image Analysis | ||||||||||||
| Publisher: | Elsevier Science BV | ||||||||||||
| ISSN: | 1361-8415 | ||||||||||||
| Official Date: | February 2019 | ||||||||||||
| Dates: |
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| Volume: | 52 | ||||||||||||
| Page Range: | pp. 199-211 | ||||||||||||
| DOI: | 10.1016/j.media.2018.12.001 | ||||||||||||
| Status: | Peer Reviewed | ||||||||||||
| Publication Status: | Published | ||||||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||||||
| RIOXX Funder/Project Grant: |
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