Dual Adaptive Pyramid Network for Cross-Stain Histopathology Image Segmentation

Hou, Xianxu; Liu, Jingxin; Xu, Bolei; Liu, Bozhi; Chen, Xin; Garibaldi, Jon; Ilyas, Mohammad; Ellis, Ian; Qiu, Guoping

Abstract

Supervised semantic segmentation normally assumes the test data being in a similar data domain as the training data. However, in practice, the domain mismatch between the training and unseen data could lead to a significant performance drop. Obtaining accurate pixel-wise label for images in different domains is tedious and labor intensive, especially for histopathology images. In this paper, we propose a dual adaptive pyramid network (DAPNet) for histopathological gland segmentation adapting from one stain domain to another. We tackle the domain adaptation problem on two levels: (1) the image-level considers the differences of image color and style; (2) the feature-level addresses the spatial inconsistency between two domains. The two components are implemented as domain classifiers with adversarial training. We evaluate our new approach using two gland segmentation datasets with H&E and DAB-H stains respectively. The extensive experiments and ablation study demonstrate the effectiveness of our approach on the domain adaptive segmentation task. We show that the proposed approach performs favorably against other state-of-the-art methods.

Citation

Hou, X., Liu, J., Xu, B., Liu, B., Chen, X., Garibaldi, J., Ilyas, M., Ellis, I., & Qiu, G. (2019, October). Dual Adaptive Pyramid Network for Cross-Stain Histopathology Image Segmentation. Presented at 22nd International Conference, Shenzhen, China

• Presentation Conference Type: Edited Proceedings
• Conference Name: 22nd International Conference
• Start Date: Oct 13, 2019
• End Date: Oct 17, 2019
• Online Publication Date: Oct 10, 2019
• Publication Date: 2019
• Deposit Date: Jan 10, 2020
• Publicly Available Date: Oct 11, 2020
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Pages: 101-109
• Series Title: Lecture Notes in Computer Science
• Series Number: 11765
• Series ISSN: 1611-3349
• Book Title: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019
• ISBN: 9783030322441
• DOI: https://doi.org/10.1007/978-3-030-32245-8_12
• Public URL: https://nottingham-repository.worktribe.com/output/3518035
• Publisher URL: https://link.springer.com/chapter/10.1007%2F978-3-030-32245-8_12