OASIS: One-pass aligned Atlas Set for Image Segmentation
Qikui Zhu, Bo Du, Pingkun Yan

TL;DR
OASIS introduces a deep learning-based one-pass aligned atlas set framework for efficient and accurate medical image segmentation, reducing registration time and focusing on relevant regions to improve performance.
Contribution
The paper presents a novel deep learning approach for one-pass image registration and a label fusion strategy, enhancing atlas-based segmentation efficiency and accuracy.
Findings
Significantly improved prostate MR segmentation accuracy
Reduced registration time compared to iterative methods
Effective focus on regions of interest during registration
Abstract
Medical image segmentation is a fundamental task in medical image analysis. Despite that deep convolutional neural networks have gained stellar performance in this challenging task, they typically rely on large labeled datasets, which have limited their extension to customized applications. By revisiting the superiority of atlas based segmentation methods, we present a new framework of One-pass aligned Atlas Set for Images Segmentation (OASIS). To address the problem of time-consuming iterative image registration used for atlas warping, the proposed method takes advantage of the power of deep learning to achieve one-pass image registration. In addition, by applying label constraint, OASIS also makes the registration process to be focused on the regions to be segmented for improving the performance of segmentation. Furthermore, instead of using image based similarity for label fusion,…
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Taxonomy
TopicsAdvanced Neural Network Applications · Medical Image Segmentation Techniques · Advanced Image and Video Retrieval Techniques
