Abstract：
:Pseudo-healthy synthesis is the task of creating a subject-specific 'healthy' image from a pathological one. Such images can be helpful in tasks such as anomaly detection and understanding changes induced by pathology and disease. In this paper, we present a model that is encouraged to disentangle the information of pathology from what seems to be healthy. We disentangle what appears to be healthy and where disease is as a segmentation map, which are then recombined by a network to reconstruct the input disease image. We train our models adversarially using either paired or unpaired settings, where we pair disease images and maps when available. We quantitatively and subjectively, with a human study, evaluate the quality of pseudo-healthy images using several criteria. We show in a series of experiments, performed on ISLES, BraTS and Cam-CAN datasets, that our method is better than several baselines and methods from the literature. We also show that due to better training processes we could recover deformations, on surrounding tissue, caused by disease. Our implementation is publicly available at https://github.com/xiat0616/pseudo-healthy-synthesis.
journal_name
Med Image Analjournal_title
Medical image analysisauthors
Xia T,Chartsias A,Tsaftaris SAdoi
10.1016/j.media.2020.101719subject
Has Abstractpub_date
2020-08-01 00:00:00pages
101719eissn
1361-8415issn
1361-8423pii
S1361-8415(20)30083-9journal_volume
64pub_type
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