Learning Global and Local Features of Normal Brain Anatomy for Unsupervised Abnormality Detection

• URL: http://arxiv.org/abs/2005.12573v3
• Date: Sat, 8 May 2021 11:45:24 GMT
• Title: Learning Global and Local Features of Normal Brain Anatomy for Unsupervised Abnormality Detection
• Authors: Kazuma Kobayashi, Ryuichiro Hataya, Yusuke Kurose, Amina Bolatkan, Mototaka Miyake, Hirokazu Watanabe, Masamichi Takahashi, Jun Itami, Tatsuya Harada, Ryuji Hamamoto
• Abstract summary: We demonstrate an unsupervised learning framework for pixel-wise abnormality detection in brain magnetic resonance imaging. Our concept is as follows: If an image reconstruction network can faithfully reproduce the global features of normal anatomy, then the abnormal lesions in unseen images can be identified. The results show that the area under the receiver operating characteristics curve values for metastatic brain tumors, extracranial metastatic tumors, postoperative cavities, and structural changes are 0.78, 0.61, 0.91, and 0.60, respectively.
• Score: 35.15777802684473
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In real-world clinical practice, overlooking unanticipated findings can result in serious consequences. However, supervised learning, which is the foundation for the current success of deep learning, only encourages models to identify abnormalities that are defined in datasets in advance. Therefore, abnormality detection must be implemented in medical images that are not limited to a specific disease category. In this study, we demonstrate an unsupervised learning framework for pixel-wise abnormality detection in brain magnetic resonance imaging captured from a patient population with metastatic brain tumor. Our concept is as follows: If an image reconstruction network can faithfully reproduce the global features of normal anatomy, then the abnormal lesions in unseen images can be identified based on the local difference from those reconstructed as normal by a discriminative network. Both networks are trained on a dataset comprising only normal images without labels. In addition, we devise a metric to evaluate the anatomical fidelity of the reconstructed images and confirm that the overall detection performance is improved when the image reconstruction network achieves a higher score. For evaluation, clinically significant abnormalities are comprehensively segmented. The results show that the area under the receiver operating characteristics curve values for metastatic brain tumors, extracranial metastatic tumors, postoperative cavities, and structural changes are 0.78, 0.61, 0.91, and 0.60, respectively.

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