Gland Instance Segmentation by Deep Multichannel Neural Networks
This work addresses the challenging problem of segmenting individual glands in medical images, which is important for histopathology analysis, but it appears incremental as it builds on existing deep learning approaches.
The paper tackles gland instance segmentation in colon histology images by proposing a deep multichannel neural network framework, achieving state-of-the-art results compared to existing methods in the 2015 MICCAI Gland Segmentation Challenge.
In this paper, we propose a new image instance segmentation method that segments individual glands (instances) in colon histology images. This is a task called instance segmentation that has recently become increasingly important. The problem is challenging since not only do the glands need to be segmented from the complex background, they are also required to be individually identified. Here we leverage the idea of image-to-image prediction in recent deep learning by building a framework that automatically exploits and fuses complex multichannel information, regional, location and boundary patterns in gland histology images. Our proposed system, deep multichannel framework, alleviates heavy feature design due to the use of convolutional neural networks and is able to meet multifarious requirement by altering channels. Compared to methods reported in the 2015 MICCAI Gland Segmentation Challenge and other currently prevalent methods of instance segmentation, we observe state-of-the-art results based on a number of evaluation metrics.