PIMedSeg: Progressive interactive medical image segmentation
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Published on: April 5, 2024
Xun Gong1, Li Wang1, Longlong Miao2
1School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, PR China; Engineering Research Center of Sustainable Urban Intelligent Transportation, Ministry of Education, Chengdu 611756, PR China; Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province, Southwest Jiaotong University, Chengdu 611756, PR China.
Background And Objective:
Accurate object segmentation in medical images is a crucial step in medical diagnosis and other applications. Despite years of research on automatic segmentation approaches, achieving clinically acceptable image quality remains challenging. Interactive segmentation is seen as a promising alternative; thus, we propose a new interactive segmentation framework based on a progressive workflow to reduce user effort and provide high-quality results.
Method:
First, our approach encodes user-provided region clicks and edge scribbles using our proposed disk and curve transform. Then, it is followed by refinement with a transformer-based module that extracts effective features from the outputs of the convolutional neural network (CNN) and the extra input maps.
Result:
Extensive experiments conducted on various medical images, including ultrasound (US), computerized tomography (CT), and magnetic resonance images (MRI), have demonstrated the effectiveness of our new approach over the state-of-the-art alternatives.
Conclusion:
The proposed framework can achieve high-quality segmentation using minimal interactions without the substantial cost of manual segmentation.
