Abstract：
:Synthesized medical images have several important applications. For instance, they can be used as an intermedium in cross-modality image registration or used as augmented training samples to boost the generalization capability of a classifier. In this work, we propose a generic cross-modality synthesis approach with the following targets: 1) synthesizing realistic looking 2D/3D images without needing paired training data, 2) ensuring consistent anatomical structures, which could be changed by geometric distortion in cross-modality synthesis and 3) more importantly, improving volume segmentation by using synthetic data for modalities with limited training samples. We show that these goals can be achieved with an end-to-end 2D/3D convolutional neural network (CNN) composed of mutually-beneficial generators and segmentors for image synthesis and segmentation tasks. The generators are trained with an adversarial loss, a cycle-consistency loss, and also a shape-consistency loss (supervised by segmentors) to reduce the geometric distortion. From the segmentation view, the segmentors are boosted by synthetic data from generators in an online manner. Generators and segmentors prompt each other alternatively in an end-to-end training fashion. We validate our proposed method on three datasets, including cardiovascular CT and magnetic resonance imaging (MRI), abdominal CT and MRI, and mammography X-rays from different data domains, showing both tasks are beneficial to each other and coupling these two tasks results in better performance than solving them exclusively.
journal_name
Med Image Analjournal_title
Medical image analysisauthors
Cai J,Zhang Z,Cui L,Zheng Y,Yang Ldoi
10.1016/j.media.2018.12.002subject
Has Abstractpub_date
2019-02-01 00:00:00pages
174-184eissn
1361-8415issn
1361-8423pii
S1361-8415(18)30342-6journal_volume
52pub_type
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