Volumetric Attention for 3D Medical Image Segmentation and Detection

• URL: http://arxiv.org/abs/2004.01997v1
• Date: Sat, 4 Apr 2020 18:55:06 GMT
• Title: Volumetric Attention for 3D Medical Image Segmentation and Detection
• Authors: Xudong Wang, Shizhong Han, Yunqiang Chen, Dashan Gao, and Nuno Vasconcelos
• Abstract summary: A volumetric attention(VA) module for 3D medical image segmentation and detection is proposed. VA attention is inspired by recent advances in video processing, enables 2.5D networks to leverage context information along the z direction. Its integration in the Mask R-CNN is shown to enable state-of-the-art performance on the Liver Tumor (LiTS) Challenge.
• Score: 53.041572035020344
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A volumetric attention(VA) module for 3D medical image segmentation and detection is proposed. VA attention is inspired by recent advances in video processing, enables 2.5D networks to leverage context information along the z direction, and allows the use of pretrained 2D detection models when training data is limited, as is often the case for medical applications. Its integration in the Mask R-CNN is shown to enable state-of-the-art performance on the Liver Tumor Segmentation (LiTS) Challenge, outperforming the previous challenge winner by 3.9 points and achieving top performance on the LiTS leader board at the time of paper submission. Detection experiments on the DeepLesion dataset also show that the addition of VA to existing object detectors enables a 69.1 sensitivity at 0.5 false positive per image, outperforming the best published results by 6.6 points.

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