Adapting Mask-RCNN for Automatic Nucleus Segmentation
This addresses the need for accurate medical image analysis in tasks like nucleus detection, but it is incremental as it applies an existing method to a new domain.
The paper tackled the problem of automatic nucleus segmentation in microscopy images by adapting Mask-RCNN, achieving highly effective and efficient segmentations across various cell types and conditions.
Automatic segmentation of microscopy images is an important task in medical image processing and analysis. Nucleus detection is an important example of this task. Mask-RCNN is a recently proposed state-of-the-art algorithm for object detection, object localization, and object instance segmentation of natural images. In this paper we demonstrate that Mask-RCNN can be used to perform highly effective and efficient automatic segmentations of a wide range of microscopy images of cell nuclei, for a variety of cells acquired under a variety of conditions.