IEEE Trans Biomed Eng. 2017 Dec;64(12):2901-2912. doi: 10.1109/TBME.2017.2686418. Epub 2017 Mar 23.
A new image instance segmentation method is proposed to segment individual glands (instances) in colon histology images. This process is challenging since the glands not only need to be segmented from a complex background, they must also be individually identified.
We leverage the idea of image-to-image prediction in recent deep learning by designing an algorithm that automatically exploits and fuses complex multichannel information-regional, location, and boundary cues-in gland histology images. Our proposed algorithm, a deep multichannel framework, alleviates heavy feature design due to the use of convolutional neural networks and is able to meet multifarious requirements by altering channels.
Compared with methods reported in the 2015 MICCAI Gland Segmentation Challenge and other currently prevalent instance segmentation methods, we observe state-of-the-art results based on the evaluation metrics.
The proposed deep multichannel algorithm is an effective method for gland instance segmentation.
The generalization ability of our model not only enable the algorithm to solve gland instance segmentation problems, but the channel is also alternative that can be replaced for a specific task.
提出了一种新的图像实例分割方法，用于分割结肠组织学图像中的单个腺体（实例）。由于腺体不仅需要从复杂的背景中分割出来，还需要单独识别，因此这个过程具有挑战性。
我们利用最近深度学习中的图像到图像预测的思想，设计了一种算法，该算法自动利用和融合腺体组织学图像中的复杂多通道信息——区域、位置和边界线索。我们提出的算法，即深度多通道框架，由于使用了卷积神经网络，因此减轻了繁重的特征设计，并通过改变通道来满足各种需求。
与 2015 年 MICCAI 腺体分割挑战赛中报告的方法和其他当前流行的实例分割方法相比，我们基于评估指标观察到了最先进的结果。
所提出的深度多通道算法是一种有效的腺体实例分割方法。
我们模型的泛化能力不仅使算法能够解决腺体实例分割问题，而且通道也是可替代的，可以为特定任务替换。
