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Automatic Nucleus Segmentation with Mask-RCNN

2019; Springer Nature; Linguagem: Inglês

10.1007/978-3-030-17798-0_32

2194-5357

Jeremiah Johnson,

Cell Image Analysis Techniques

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, it is demonstrated 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. In addition, it is shown that a cyclic learning rate regime allows effective training of a Mask-RCNN model without any need to finetune the learning rate, thereby eliminating a manual and time-consuming aspect of the training procedure. The results presented here will be of interest to those in the medical imaging field and to computer vision researchers more generally.