Region Proposal Rectification Towards Robust Instance Segmentation of Biological Images
This addresses incomplete segmentation for biological image analysis, enabling better extraction of morphological properties like shapes and volumes, but it is incremental as it builds on existing top-down frameworks.
The paper tackles the over-crop problem in top-down instance segmentation for biological images by proposing a region proposal rectification module, which improves segmentation performance on three datasets compared to state-of-the-art methods.
Top-down instance segmentation framework has shown its superiority in object detection compared to the bottom-up framework. While it is efficient in addressing over-segmentation, top-down instance segmentation suffers from over-crop problem. However, a complete segmentation mask is crucial for biological image analysis as it delivers important morphological properties such as shapes and volumes. In this paper, we propose a region proposal rectification (RPR) module to address this challenging incomplete segmentation problem. In particular, we offer a progressive ROIAlign module to introduce neighbor information into a series of ROIs gradually. The ROI features are fed into an attentive feed-forward network (FFN) for proposal box regression. With additional neighbor information, the proposed RPR module shows significant improvement in correction of region proposal locations and thereby exhibits favorable instance segmentation performances on three biological image datasets compared to state-of-the-art baseline methods. Experimental results demonstrate that the proposed RPR module is effective in both anchor-based and anchor-free top-down instance segmentation approaches, suggesting the proposed method can be applied to general top-down instance segmentation of biological images.