Multi-scale Multi-structure Siamese Network (MMSNet) for Primary Open-Angle Glaucoma Prediction

Mingquan Lin, Lei Liu, Mae Gordon, Michael Kass, Sarah Van Tassel, Fei Wang, Yifan Peng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Primary open-angle glaucoma (POAG) is one of the leading causes of irreversible blindness in the United States and worldwide. POAG prediction before onset plays an important role in early treatment. Although deep learning methods have been proposed to predict POAG, these methods mainly focus on current status prediction. In addition, all these methods used a single image as input. On the other hand, glaucoma specialists determine a glaucomatous eye by comparing the follow-up optic nerve image with the baseline along with supplementary clinical data. To simulate this process, we proposed a Multi-scale Multi-structure Siamese Network (MMSNet) to predict future POAG event from fundus photographs. The MMSNet consists of two side-outputs for deep supervision and 2D blocks to utilize two-dimensional features to assist classification. The MMSNet network was trained and evaluated on a large dataset: 37,339 fundus photographs from 1,636 Ocular Hypertension Treatment Study (OHTS) participants. Extensive experiments show that MMSNet outperforms the state-of-the-art on two “POAG prediction before onset” tasks. Our AUC are 0.9312 and 0.9507, which are 0.2204 and 0.1490 higher than the state-of-the-art, respectively. In addition, an ablation study is performed to check the contribution of different components. These results highlight the potential of deep learning to assist and enhance the prediction of future POAG event. The proposed network will be publicly available on https://github.com/bionlplab/MMSNet.

Original languageEnglish
Title of host publicationMachine Learning in Medical Imaging - 13th International Workshop, MLMI 2022, Held in Conjunction with MICCAI 2022, Proceedings
EditorsChunfeng Lian, Xiaohuan Cao, Islem Rekik, Xuanang Xu, Zhiming Cui
PublisherSpringer Science and Business Media Deutschland GmbH
Pages436-445
Number of pages10
ISBN (Print)9783031210136
DOIs
StatePublished - 2022
Event13th International Workshop on Machine Learning in Medical Imaging, MLMI 2022, held in conjunction with 25th International Conference on Medical Image Computing and Computer_Assisted Intervention, MICCAI 2022 - Singapore, Singapore
Duration: Sep 18 2022Sep 18 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13583 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Workshop on Machine Learning in Medical Imaging, MLMI 2022, held in conjunction with 25th International Conference on Medical Image Computing and Computer_Assisted Intervention, MICCAI 2022
Country/TerritorySingapore
CitySingapore
Period09/18/2209/18/22

Keywords

  • Deep learning
  • Fundus photographs
  • Primary open-angle glaucoma (POAG)
  • Siamese network

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