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Proceedings ArticleDOI

Gland segmentation from histology images using informative morphological scale space

01 Sep 2016-pp 4121-4125
TL;DR: This work proposes an automated solution for gland segmentation from hematoxylin & eosin (H&E) stained histology images based on a novel informative morphological scale space that uses the entropy of the connected components in a novel manner to prevent over segmentation of objects.
Abstract: Grading of cancer offers insight to the occurrence and progress of the disease. The course of treatment is planned depending on the grade of cancer. Segmentation of the glandular structure of tissue is a prerequisite for grading of colon, prostate and breast cancers. Manual segmentation method is time-consuming and suffers from the curse of observer bias. We propose an automated solution for gland segmentation from hematoxylin & eosin (H&E) stained histology images. Our method relies on the biological cue rather than gland specific signatures that may vary across the slides. We construct a novel informative morphological scale space for gland segmentation. The scale space uses the entropy of the connected components in a novel manner to prevent over segmentation of objects. Our solution is fast, accurate and applicable in a clinical setup. Experiments show an average F1 score of 0.68 for 85 histology images in 20x magnification. We obtain ∼ 30% improvement in F1 score compared to the area morphological scale space method.
Citations
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Journal ArticleDOI
Yan Xu1, Yang Li1, Yipei Wang1, Mingyuan Liu1, Yubo Fan1, Maode Lai, Eric Chang2 
TL;DR: An algorithm that automatically exploits and fuses complex multichannel information—regional, location, and boundary cues—in gland histology images and is able to meet multifarious requirements by altering channels is proposed.
Abstract: Objective: 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. Methods: 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. Results: 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. Conclusion: The proposed deep multichannel algorithm is an effective method for gland instance segmentation. Significance: 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.

153 citations


Cites methods from "Gland segmentation from histology i..."

  • ...…of gland instance segmentation, is a well-studied field in which various methods have been explored, such as morphology-based methods (Naik et al., 2007; Nguyen et al., 2010; Naik et al., 2008; Paul and Mukherjee, 2016) and graph-based methods (Egger, 2013; Tosun and Gunduz-Demir, 2011)....

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  • ...Gland labeling/segmentation, as one subproblem of gland instance segmentation, is a well-studied field where various methods have been explored, such as morphology-based methods [6]–[9] and graph-based methods [10], [11]....

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Journal ArticleDOI
Thomas Binder1, El Mehdi Tantaoui1, Pushpak Pati1, Raul Catena1, Ago Set-Aghayan1, Maria Gabrani1 
TL;DR: This work investigates the idea of cross-domain (-organ type) approximation that aims at reducing the need for organ-specific annotations in segmenting glands, and hypothesized that an automatic method, that can precisely segment the stroma, would pave the way for a cross-organ gland segmentation.
Abstract: Clinical morphological analysis of histopathology samples is an effective method in cancer diagnosis. Computational pathology methods can be employed to automate this analysis, providing improved objectivity and scalability. More specifically, computational techniques can be used in segmenting glands, which is an essential factor in cancer diagnosis. Automatic delineation of glands is a challenging task considering a large variability in glandular morphology across tissues and pathological subtypes. A deep learning based gland segmentation method can be developed to address the above task, but it requires a large number of accurate gland annotations from several tissue slides. Such a large dataset need to be generated manually by experienced pathologists, which is laborious, time-consuming, expensive, and suffers from the subjectivity of the annotator. So far, deep learning techniques have produced promising results on a few organ-specific gland segmentation tasks, however, the demand for organ-specific gland annotations hinder the extensibility of these techniques to other organs. This work investigates the idea of cross-domain (-organ type) approximation that aims at reducing the need for organ-specific annotations. Unlike parenchyma, the stromal component of tissues, that lies between the glands, is more consistent across several organs. It is hypothesized that an automatic method, that can precisely segment the stroma, would pave the way for a cross-organ gland segmentation. Two proposed Dense-U-Nets are trained on H&E strained colon adenocarcinoma samples focusing on the gland and stroma segmentation. The trained networks are evaluated on two independent datasets, they are, a H&E stained colon adenocarcinoma dataset and a H&E stained breast invasive cancer dataset. The trained network targeting the stroma segmentation performs similar to the network targeting the gland segmentation on the colon dataset. Whereas, the former approach performs significantly better compared to the latter approach on the breast dataset, showcasing the higher generalization capacity of the stroma segmentation approach. The networks are evaluated using Dice coefficient and Hausdorff distance computed between the ground truth gland masks and the predicted gland masks. The conducted experiments validate the efficacy of the proposed stoma segmentation approach toward multi-organ gland segmentation.

35 citations

Proceedings ArticleDOI
04 Apr 2018
TL;DR: By evaluating quantitative individual cell segmentation results on 2017 MICCAI Digital Pathology Challenge, the proposed dual contour-enhanced adversarial network achieved best balance between precision and recall rate of individualcell segmentation compared to state-of-the-art cell segmentations methods.
Abstract: The morphology of cancer cells is widely used by pathologists to grade stages of cancers. Accurate cancer cell segmentation is significant to obtain quantitative diagnosis. We proposed a dual contour-enhanced adversarial network to solve this challenge. The distance-transformed and contour-highlighted masks, and adversarial network are incorporated to improve individual cell segmentation capability. By evaluating quantitative individual cell segmentation results on 2017 MICCAI Digital Pathology Challenge, our method achieved best balance between precision and recall rate of individual cell segmentation compared to state-of-the-art cell segmentation methods.

23 citations


Cites methods from "Gland segmentation from histology i..."

  • ...Cell segmentation of pathology images usually applies morphology based [1, 2] or patch-based pixel level classification [3] methods....

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Proceedings ArticleDOI
01 Jul 2019
TL;DR: In this article, a modified version of LinkNet was proposed for gland segmentation and recognition of malignant cases in histopathology images using specific handcrafted features such as invariant local binary pattern.
Abstract: Histopathology images contain essential information for medical diagnosis and prognosis of cancerous disease. Segmentation of glands in histopathology images is a primary step for analysis and diagnosis of an unhealthy patient. Due to the widespread application and the great success of deep neural networks in intelligent medical diagnosis and histopathology, we propose a modified version of LinkNet for gland segmentation and recognition of malignant cases. We show that using specific handcrafted features such as invariant local binary pattern drastically improves the system performance. The experimental results demonstrate the competency of the proposed system against the state-of-the-art methods. We achieved the best results in testing on section B images of the Warwick-QU dataset and obtained comparable results on section A images.

16 citations

Posted Content
TL;DR: It is shown that using specific handcrafted features such as invariant local binary pattern drastically improves the system performance and demonstrates the competency of the proposed system against the state-of-the-art methods.
Abstract: Histopathology images contain essential information for medical diagnosis and prognosis of cancerous disease. Segmentation of glands in histopathology images is a primary step for analysis and diagnosis of an unhealthy patient. Due to the widespread application and the great success of deep neural networks in intelligent medical diagnosis and histopathology, we propose a modified version of LinkNet for gland segmentation and recognition of malignant cases. We show that using specific handcrafted features such as invariant local binary pattern drastically improves the system performance. The experimental results demonstrate the competency of the proposed system against state-of-the-art methods. We achieved the best results in testing on section B images of the Warwick-QU dataset and obtained comparable results on section A images.

11 citations


Cites background or methods from "Gland segmentation from histology i..."

  • ...[2] used informative morphological scale space to segment glands....

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  • ...Some of the researches propose to use structural information of glands for segmentation [1], [2]....

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  • ...Segmentation based on the structure of glands is faster compared to segmentation based on a neural network [2]....

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References
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Journal ArticleDOI
TL;DR: The data for stages III and IV patients with histologically low grade cancers suggest that these patients are at no greater risk of death from cancer than most stages I and II patients for whom radical prostatectomy has been recommended.

2,029 citations


"Gland segmentation from histology i..." refers background in this paper

  • ...Index Terms— Gland segmentation, informative morphological scale space, edge preserving filter....

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BookDOI
01 Mar 1995

671 citations

Journal ArticleDOI
TL;DR: This review article focuses on the histopathology and molecular pathology of colorectal carcinoma and its precursor lesions, with an emphasis on their clinical relevance.
Abstract: Colorectal carcinoma is one of the most common cancers and one of the leading causes of cancer-related death in the United States. Pathologic examination of biopsy, polypectomy and resection specimens is crucial to appropriate patient managemnt, prognosis assessment and family counseling. Molecular testing plays an increasingly important role in the era of personalized medicine. This review article focuses on the histopathology and molecular pathology of colorectal carcinoma and its precursor lesions, with an emphasis on their clinical relevance.

551 citations


"Gland segmentation from histology i..." refers background in this paper

  • ...Index Terms— Gland segmentation, informative morphological scale space, edge preserving filter....

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Journal ArticleDOI
TL;DR: Gleason grade of adenocarcinoma of the prostate is an established prognostic indicator that has stood the test of time and is routinely used to plan patient management and is also one of the criteria for eligibility for clinical trials testing new therapies.

523 citations


"Gland segmentation from histology i..." refers background in this paper

  • ...Index Terms— Gland segmentation, informative morphological scale space, edge preserving filter....

    [...]

Proceedings ArticleDOI
14 May 2008
TL;DR: The utility of the glandular and nuclear segmentation algorithm in accurate extraction of various morphological and nuclear features for automated grading of prostate cancer, breast cancer, and breast cancer specimens is demonstrated by distinguishing between cancerous and benign breast histology specimens.
Abstract: Automated detection and segmentation of nuclear and glandular structures is critical for classification and grading of prostate and breast cancer histopathology. In this paper, we present a methodology for automated detection and segmentation of structures of interest in digitized histopathology images. The scheme integrates image information from across three different scales: (1) low- level information based on pixel values, (2) high-level information based on relationships between pixels for object detection, and (3) domain-specific information based on relationships between histological structures. Low-level information is utilized by a Bayesian classifier to generate a likelihood that each pixel belongs to an object of interest. High-level information is extracted in two ways: (i) by a level-set algorithm, where a contour is evolved in the likelihood scenes generated by the Bayesian classifier to identify object boundaries, and (ii) by a template matching algorithm, where shape models are used to identify glands and nuclei from the low-level likelihood scenes. Structural constraints are imposed via domain- specific knowledge in order to verify whether the detected objects do indeed belong to structures of interest. In this paper we demonstrate the utility of our glandular and nuclear segmentation algorithm in accurate extraction of various morphological and nuclear features for automated grading of (a) prostate cancer, (b) breast cancer, and (c) distinguishing between cancerous and benign breast histology specimens. The efficacy of our segmentation algorithm is evaluated by comparing breast and prostate cancer grading and benign vs. cancer discrimination accuracies with corresponding accuracies obtained via manual detection and segmentation of glands and nuclei.

335 citations


"Gland segmentation from histology i..." refers background in this paper

  • ...Index Terms— Gland segmentation, informative morphological scale space, edge preserving filter....

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