End-to-end detection-segmentation network with ROI convolution
This work addresses segmentation challenges for small objects in clinical ultrasound imaging, representing an incremental improvement over existing methods.
The authors tackled the problem of segmenting small objects in ultrasound images by proposing an end-to-end network that jointly learns detection and segmentation, resulting in improved segmentation accuracy compared to segmentation-only methods.
We propose an end-to-end neural network that improves the segmentation accuracy of fully convolutional networks by incorporating a localization unit. This network performs object localization first, which is then used as a cue to guide the training of the segmentation network. We test the proposed method on a segmentation task of small objects on a clinical dataset of ultrasound images. We show that by jointly learning for detection and segmentation, the proposed network is able to improve the segmentation accuracy compared to only learning for segmentation. Code is publicly available at https://github.com/vincentzhang/roi-fcn.