Segmentation-by-Detection: A Cascade Network for Volumetric Medical Image Segmentation
This addresses efficient segmentation for medical imaging with low signal-to-noise ratio, but it is incremental as it builds on existing detection and segmentation paradigms.
The authors tackled 3D medical image segmentation by proposing a cascade network that first detects regions of interest to reduce noise, then segments within those regions, showing superiority over U-Net on 3D ultrasound data of the femoral head.
We propose an attention mechanism for 3D medical image segmentation. The method, named segmentation-by-detection, is a cascade of a detection module followed by a segmentation module. The detection module enables a region of interest to come to attention and produces a set of object region candidates which are further used as an attention model. Rather than dealing with the entire volume, the segmentation module distills the information from the potential region. This scheme is an efficient solution for volumetric data as it reduces the influence of the surrounding noise which is especially important for medical data with low signal-to-noise ratio. Experimental results on 3D ultrasound data of the femoral head shows superiority of the proposed method when compared with a standard fully convolutional network like the U-Net.