OASIS: One-pass aligned Atlas Set for Image Segmentation
This work addresses the need for efficient and accurate segmentation in medical imaging, particularly for customized applications, though it is incremental in combining atlas-based methods with deep learning.
The paper tackled the problem of medical image segmentation by proposing OASIS, a framework that uses deep learning for one-pass image registration and label similarity-based fusion, which significantly increased segmentation performance on prostate MR images compared to state-of-the-art methods.
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, which can be distracted by the large background areas, we propose a novel strategy to compute the label similarity based weights for label fusion. Our experimental results on the challenging task of prostate MR image segmentation demonstrate that OASIS is able to significantly increase the segmentation performance compared to other state-of-the-art methods.