Adapting Mask-RCNN for Automatic Nucleus Segmentation

TL;DR

This paper demonstrates that Mask-RCNN, a state-of-the-art object detection algorithm, can be effectively adapted for automatic segmentation of cell nuclei in microscopy images across diverse conditions.

Contribution

The study adapts Mask-RCNN for nucleus segmentation, showing its effectiveness in microscopy images, which is a novel application of this algorithm.

Findings

High segmentation accuracy across various microscopy datasets

Efficient processing suitable for large-scale analysis

Versatility in handling different cell types and imaging conditions

Abstract

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.