A Stochastic Polygons Model for Glandular Structures in Colon Histology Images

Sirinukunwattana, Korsuk; Snead, David; Rajpoot, Nasir M.

doi:10.1109/tmi.2015.2433900

Cited by 231 publications

(141 citation statements)

References 25 publications

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“…Leveraging the efficient inference of fully convolutional architecture, the average time for processing one testing image with size 755 × 522 was about 1.5 seconds, which was much faster than other methods [35,20] in the literature. Considering large-scale histology images are demanded for prompt analysis with the advent of whole slide imaging, the fast speed implies the possibility of our method in clinical practice.…”

Section: Computation Costmentioning

confidence: 94%

“…Third, in the malignant cases such as moderately and poorly differentiated adenocarcinomas, the glandular structures are seriously degenerated, as shown in Fig-ure 1 (bottom left). Therefore, methods utilizing the prior knowledge with glandular regularity are prone to fail in such cases [35]. In addition, the variation of tissue preparation procedures such as sectioning and staining can cause deformation, artifacts and inconsistency of tissue appearance, which can impede the segmentation process as well.…”

Section: Introductionmentioning

confidence: 99%

“…For this kind of method, various hand-crafted features including texture, color, morphological cues and Haar-like features were utilized to detect the glandular structure from histology images [11,38,13,36,37,28,23,32]; (2) structure based methods. Most of approaches in this category take advantage of prior knowledge about the glandular structure, such as graph based methods [2,20], glandular boundary delineation with geodesic distance transform [16], polar space random field model [18], stochastic polygons model [35], etc. Although these methods achieved promising results in cases of adenoma and well differentiated (low grade) adenocarcinoma, they may fail to achieve satisfying performance in malignant subjects, where the glandular structures are seriously deformed.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation

Chen

et al. 2016

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

523

339

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The morphology of glands has been used routinely by pathologists to assess the malignancy degree of adenocarcinomas. Accurate segmentation of glands from histology images is a crucial step to obtain reliable morphological statistics for quantitative diagnosis. In this paper, we proposed an efficient deep contour-aware network (DCAN) to solve this challenging problem under a unified multi-task learning framework. In the proposed network, multi-level contextual features from the hierarchical architecture are explored with auxiliary supervision for accurate gland segmentation. When incorporated with multi-task regularization during the training, the discriminative capability of intermediate features can be further improved. Moreover, our network can not only output accurate probability maps of glands, but also depict clear contours simultaneously for separating clustered objects, which further boosts the gland segmentation performance. This unified framework can be efficient when applied to large-scale histopathological data without resorting to additional steps to generate contours based on low-level cues for post-separating. Our method won the 2015 MICCAI Gland Segmentation Challenge out of 13 competitive teams, surpassing all the other methods by a significant margin.

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Section: Computation Costmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation

Chen

et al. 2016

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

523

339

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show abstract

“…Previous works focus on detecting gland structure like nuclei and lumen. Sirinukunwattana et al [27] model every gland as a polygon in which the vertices are located at the nucleus. Cheikh et al [28] propose a mathematical morphology method to characterize the spatial distribution of nuclei in histological images.…”

Section: B Gland Instance Segmentationmentioning

confidence: 99%

“…Nguyen et al [29] use texture and structural features to classify the basic components of glands, and then segment gland instance based on prior knowledge of gland structure. These methods perform well in benign images but are comparatively unsatisfactory when used on malignant images, which has been the impetus for creating methods based on deep learning [27]. Li et al [30] train a window-based binary classifier to segment glands using both CNN features and hand-crafted features.…”

Section: B Gland Instance Segmentationmentioning

confidence: 99%

Gland Instance Segmentation Using Deep Multichannel Neural Networks

Xu,

Li,

Wang

et al. 2017

IEEE Trans. Biomed. Eng.

136

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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 to methods reported in the 2015 MICCAI Gland Segmentation Challenge and other currently prevalent instance segmentation methods, we observe state-ofthe-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.

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A survey on artificial intelligence in histopathology image analysis

Abdelsamea

Zidan

Senousy

et al. 2022

WIREs Data Min & Knowl

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The increasing adoption of the whole slide image (WSI) technology in histopathology has dramatically transformed pathologists' workflow and allowed the use of computer systems in histopathology analysis. Extensive research in Artificial Intelligence (AI) with a huge progress has been conducted resulting in efficient, effective, and robust algorithms for several applications including cancer diagnosis, prognosis, and treatment. These algorithms offer highly accurate predictions but lack transparency, understandability, and actionability. Thus, explainable artificial intelligence (XAI) techniques are needed not only to understand the mechanism behind the decisions made by AI methods and increase user trust but also to broaden the use of AI algorithms in the clinical setting. From the survey of over 150 papers, we explore different AI algorithms that have been applied and contributed to the histopathology image analysis workflow. We first address the workflow of the histopathological process. We present an overview of various learning-based, XAI, and actionable techniques relevant to deep learning methods in histopathological imaging. We also address the evaluation of XAI methods and the need to ensure their reliability on the field.

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A Stochastic Polygons Model for Glandular Structures in Colon Histology Images

Cited by 231 publications

References 25 publications

DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation

DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation

Gland Instance Segmentation Using Deep Multichannel Neural Networks

A survey on artificial intelligence in histopathology image analysis

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