A 3D Coarse-to-Fine Framework for Volumetric Medical Image Segmentation
This work addresses the problem of accurate and reliable 3D medical image segmentation for clinical applications, representing an incremental advance over existing methods.
The paper tackles the challenge of segmenting volumetric medical images by proposing a 3D coarse-to-fine framework using 3D Convolutional Neural Networks, achieving state-of-the-art results with an average improvement of over 2% in Dice-Sørensen Coefficient on the NIH pancreas dataset and a worst-case improvement of 7% to nearly 70%.
In this paper, we adopt 3D Convolutional Neural Networks to segment volumetric medical images. Although deep neural networks have been proven to be very effective on many 2D vision tasks, it is still challenging to apply them to 3D tasks due to the limited amount of annotated 3D data and limited computational resources. We propose a novel 3D-based coarse-to-fine framework to effectively and efficiently tackle these challenges. The proposed 3D-based framework outperforms the 2D counterpart to a large margin since it can leverage the rich spatial infor- mation along all three axes. We conduct experiments on two datasets which include healthy and pathological pancreases respectively, and achieve the current state-of-the-art in terms of Dice-Sørensen Coefficient (DSC). On the NIH pancreas segmentation dataset, we outperform the previous best by an average of over 2%, and the worst case is improved by 7% to reach almost 70%, which indicates the reliability of our framework in clinical applications.