Abstract
Glands segmentation is a very important and yet challenging problem of histological images analysis. Accurate segmentation of mucous glands is a crucial step to obtain reliable morphological statistics and is necessary for the development of high-quality diagnostic algorithms, which are an integral part of timely medical care. In this paper we propose a two-stage segmentation method, which predicts the probability maps of glands boundaries in histological images based on a priori knowledge about the geometric shape of the mucous glands and uses a convolutional neural network (CNN) model to get the final segmentation result based on the predicted probability maps. The proposed method demonstrates good results in separating adjacent glands, which is one of the most challenging aspects in automatic segmentation of histological glands and one of the most complicated for algorithms based on applying convolutional neural networks. The evaluation of the proposed algorithm was performed with Warwick-QU dataset, which contains real histological images of colon tissue.
Highlights
Over the past decade a notable reduction in colorectal cancer mortality was achieved
In this paper we propose an adaptive method of glands segmentation on histological images that considers the form of histological glandular structures and evaluate the implementation of the proposed method on a real histological dataset
Krylov All existing techniques of histological gland segmentation can be divided into three main groups: 1. methods based only on mathematical principles of image processing, 2. methods that are built upon classical image processing methods with embedding of machine learning approaches at one or several stages, 3. methods that are entirely based on neural network model usage
Summary
Over the past decade a notable reduction in colorectal cancer mortality was achieved. Methods that use classical methods and machine learning in conjunction analyze structural features of glands using a prior knowledge about their relationships [3, 4]. This allows to avoid problems that arise in texture based analysis methods. Considering the information about grandular structure allows to achieve better results in comparison to texture based methods, a serious limitation of this method lies in the inaccuracy of levelset method. In [8] multi-level contextual features with auxiliary supervision detection for generating likelihood maps of glands were used They were found with end-to-end trained fully convolutional network (FCN). After that the received areas, that are candidates to be glands, are classified with a convolutional neural network
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