Retina-TransNet: A Gradient-Guided Few-Shot Retinal Vessel Segmentation Net

Due to the high labor cost of physicians, it is difficult to collect a rich amount of manually-labeled medical images for developing learning-based computer-aided diagnosis (CADx) systems or segmentation algorithms. To tackle this issue, we reshape the image segmentation task as an image-to-image (I...

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Bibliographic Details
Published in:IEEE journal of biomedical and health informatics 2023-10, Vol.PP (10), p.1-12
Main Authors: Shao, Hao-Chiang, Chen, Chih-Ying, Chang, Meng-Hsuan, Yu, Chih-Han, Lin, Chia-Wen, Yang, Ju-Wen
Format: Article
Language:English
Subjects:
Algorithms
Blood vessels
CAI
Computer assisted instruction
Datasets
Fundus photograghy
Generators
Image processing
Image segmentation
Image-to-image translation
Medical imaging
Retina
Retinal vessels
Retinopathy
Segmentation
Task analysis
Training
Training data
Translation
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Summary:Due to the high labor cost of physicians, it is difficult to collect a rich amount of manually-labeled medical images for developing learning-based computer-aided diagnosis (CADx) systems or segmentation algorithms. To tackle this issue, we reshape the image segmentation task as an image-to-image (I2I) translation problem and propose a retinal vascular segmentation network, which can achieve good cross-domain generalizability even with a small amount of training data. We devise primarily two components to facilitate this I2I-based segmentation method. The first is the constraints provided by the proposed gradient-vector-flow (GVF) loss, and, the second is a twostage Unet (2Unet) generator with a skip connection. This configuration makes 2Unet's first-stage play a role similar to conventional Unet, but forces 2Unet's second stage to learn to be a refinement module. Extensive experiments show that by re-casting retinal vessel segmentation as an image-to-image translation problem, our I2I translator-based segmentation subnetwork achieves better crossdomain generalizability than existing segmentation methods. Our model, trained on one dataset, e.g., DRIVE, can produce segmentation results stably on datasets of other domains, e.g., CHASE-DB1, STARE, HRF, and DIARETDB1, even in low-shot circumstances.
ISSN:2168-2194
2168-2208
DOI:10.1109/JBHI.2023.3298710