A structure-based approach for colon gland segmentation in digital pathology (original) (raw)
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Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest
Colorectal adenocarcinoma originating in intestinal glandular structures is the most common form of colon cancer. In clinical practice, the morphology of intestinal glands, including architectural appearance and glandular formation, is used by pathologists to inform prognosis and plan the treatment of individual patients. However, achieving good inter-observer as well as intra-observer reproducibility of cancer grading is still a major challenge in modern pathology. An automated approach which quantifies the morphology of glands is a solution to the problem. This paper provides an overview to the Gland Segmentation in Colon Histology Images Challenge Contest (GlaS) held at MICCAI’2015. Details of the challenge, including organisation, dataset and evaluation criteria, are presented, along with the method descriptions and evaluation results from the top performing methods.
Glandular Morphometrics for Objective Grading of Colorectal Adenocarcinoma Histology Images
Scientific reports, 2017
Determining the grade of colon cancer from tissue slides is a routine part of the pathological analysis. In the case of colorectal adenocarcinoma (CRA), grading is partly determined by morphology and degree of formation of glandular structures. Achieving consistency between pathologists is difficult due to the subjective nature of grading assessment. An objective grading using computer algorithms will be more consistent, and will be able to analyse images in more detail. In this paper, we measure the shape of glands with a novel metric that we call the Best Alignment Metric (BAM). We show a strong correlation between a novel measure of glandular shape and grade of the tumour. We used shape specific parameters to perform a two-class classification of images into normal or cancerous tissue and a three-class classification into normal, low grade cancer, and high grade cancer. The task of detecting gland boundaries, which is a prerequisite of shape-based analysis, was carried out using ...
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The morphology of epithelial cells plays a vital role in distinguishing malignant colon tissues from the normal ones. Epithelial cells have near elliptic shape in normal colon tissues, whereas they deform into an amorphous shape in malignant tissues. The information about the morphology of epithelial cells may be incorporated in order to obtain an effective segmentation of colon biopsy images. In this research study, we propose a novel colon biopsy image segmentation and classification (CBISC) technique that does so. The proposed CBISC technique comprises two main modules, namely, segmentation and classification. The segmentation module exploits the background information about morphology of epithelial cells, and detects elliptic and nearly elliptic epithelial cells in four orientations. It further calculates three novel features, namely, semi-major axis, direction, and area occurrence for each image pixel. Finally, it grows and merges regions based on these features, and demarcates final region boundaries. Genetic algorithm has been employed to optimize several parameters used in the segmentation process. A dataset comprising 300 colon biopsy images has been used for the evaluation of proposed segmentation module, and improved performance has been observed compared to previously reported techniques. To validate the effectiveness of segmentation, moments of gray-level histogram and gray-level co-occurrence matrix-based features have been extracted from 710 segmented patches of the B Saima Rathore
A novel approach for colon biopsy image segmentation
2013 ICME International Conference on Complex Medical Engineering
Colon cancer is one of the leading causes of deaths worldwide. Traditionally, colon cancer is diagnosed using microscopic analysis of colon biopsy images. However, computer based diagnosis involves acquiring a biopsy image, segmenting the image into constituent regions, extracting features, and based on features identifying cancerous and non-cancerous regions. Image segmentation that is the core process in overall diagnosis is extremely challenging due to similar color distribution in various biological regions of histopathological images. Problem gets more complicated for homogenous images or images acquired under different conditions, particularly change in magnification factor. Several segmentation schemes, proposed for colon images, do not address these problems. In this research study, we propose an un-supervised colon biopsy image segmentation scheme that incorporates background knowledge of benign and malignant tissue organization. The scheme detects elliptical epithelial cells in four angular directions of 0 o , 45 o , 90 o , 135 o , and divides elliptic constituents into distinct 'primitives. It further makes use of the distribution as well as spatial relations of these 'primitives' to define a homogeneity measure for identifying regions. Contrary to previous ones, the proposed scheme removes dependency on change in magnification and image type. Genetic algorithm (GA) has been employed to optimize several system parameters such as semi-major and semi-minor axis of ellipse, component area threshold to remove smaller components, and merge factor to merge two adjacent and similar regions. Algorithm has been tested on 100 colon biopsy images and improved segmentation accuracy has been observed when compared with segmentation results obtained using circular primitive based techniques.
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The objective of semantic segmentation in microscopic images is to extract the cellular, nuclear or tissue components. This problem is challenging due to the large variations of these components features (size, shape, orientation or texture). In this paper we present an automatic technique to robustly identify the epithelial nuclei (crypt) against interstitial nuclei in microscopic images taken from colon tissues. The relationship between the histological structures (epithelial layer, lumen and stroma) and the ring like shape of the crypt are considered. The crypt inner boundary is detected using a closing morphological hierarchy and its associated binary hierarchy. The outer border is determined by the epithelial nuclei, overlapped by the maximal isoline of the inner boundary. The evaluation of the proposed method is made by computing the percentage of the mis-segmented nuclei against epithelial nuclei per crypt.
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Synthesis of Annotated Colorectal Cancer Tissue Images from Gland Layout
arXiv (Cornell University), 2023
Generating annotated pairs of realistic tissue images along with their annotations is a challenging task in computational histopathology. Such synthetic images and their annotations can be useful in training and evaluation of algorithms in the domain of computational pathology. To address this, we present an interactive framework to generate pairs of realistic colorectal cancer histology images with corresponding tissue component masks from the input gland layout. The framework shows the ability to generate realistic qualitative tissue images preserving morphological characteristics including stroma, goblet cells and glandular lumen. We show the appearance of glands can be controlled by user inputs such as number of glands, their locations and sizes. We also validate the quality of generated annotated pair with help of the gland segmentation algorithm.
Microscopy Microanalysis Microstructures, 1996
2014 Une stratégie générale de segmentation combinant des critères locaux et globaux dans un processus de croissance dérivé de la ligne de partage des eaux est proposée. Les informations prises en compte sont de type contour-region ou couleur. La méthode est appliquée à différents cas d'images microscopiques de cytologie et d'histologie. Abstract. 2014 A general segmentation strategy allowing to blend multiple criteria such as contourregion or color information in a region growing process derived from the watershed transformation is proposed. It is applied onto different types of cytological and histological microscopic images.
Lecture Notes in Computer Science, 2019
Analysis of tubular glands plays an important role for gastric cancer diagnosis, grading, and prognosis; however, gland quantification is a highly subjective task, prone to error. Objective identification of glans might help clinicians for analysis and treatment planning. The visual characteristics of such glands suggest that information from nuclei and their context would be useful to characterize them. In this paper we present a new approach for segmentation of gland nuclei based on nuclear local and contextual (neighborhood) information. A Gradient-Boosted-Regression-Trees classifier is trained to distinguish between gland-nuclei and non-gland-nuclei. Validation was carried out using a dataset containing 45702 annotated nuclei from 90 1024 × 1024 fields of view extracted from gastric cancer whole slide images. A Deep Learning model was trained as a baseline. Results showed an accuracy and f-score 5.4% and 23.6% higher, respectively, with the presented framework than with the Deep Learning approach.