Gland segmentation in colon histology images: The glas challenge contest

Sirinukunwattana, Korsuk; Pluim, Josien P. W.; Chen, Hao; Qi, Xiaojuan; Heng, Pheng‐Ann; Guo, Yun; Wang, Li Yang; Matuszewski, Bogdan J.; Bruni, Elia; Sanchez, Urko; Böhm, Anton; Ronneberger, Olaf; Cheikh, Bassem Ben; Racoceanu, Daniel; Kainz, Philipp; Pfeiffer, Michael; Urschler, Martin; Snead, David; Rajpoot, Nasir M.

doi:10.1016/j.media.2016.08.008

Cited by 666 publications

(418 citation statements)

References 49 publications

Supporting

Mentioning

392

Contrasting

Order By: Relevance

“…We evaluated our method on the public benchmark dataset of Gland Segmentation Challenge Contest in MIC-CAI 2015 (also named as Warwick-QU dataset) [34]. The images were acquired by a Zeiss MIRAX MIDI slide scanner from colorectal cancer tissues with a resolution of 0.62µm/pixel.…”

Section: Dataset and Pre-processingmentioning

confidence: 99%

DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation

Chen

et al. 2016

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

Self Cite

512

339

View full text Add to dashboard Cite

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.

show abstract

Section: Dataset and Pre-processingmentioning

confidence: 99%

DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation

Chen

et al. 2016

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

Self Cite

512

339

View full text Add to dashboard Cite

show abstract

“…Reasons of space and the scope of this paper mean we cannot adequately compare and contrast other segmentation methods and the various validation methods used in the literature here, though we acknowledge there is a large body of literature on the subject, e.g. [13,14]. However, none of these frameworks adopt the topological framework assumed here, nor do they address systematic methods of resegmentation to bring the results of initial segmentation into line with the expectations of histological theory, which is the main focus of the present paper.…”

Section: Introductionmentioning

confidence: 99%

Model-Based Correction of Segmentation Errors in Digitised Histological Images

Randell

Galton

Fouad

et al. 2017

Communications in Computer and Information Science

View full text Add to dashboard Cite

Abstract. This paper describes an application of topological, modelbased methods for the algorithmic correction of segmentation errors in digitised histological images. The topological analysis is provided by the spatial logic Discrete Mereotopology and integrates qualitative spatial reasoning and constraint satisfaction methods with classical image processing methods. A set of eight topological relations defined on binary segmented regions are factored out and reworked as nodes of a set of directed graphs. The graphs encode and constrain a set of set-theoretic and topological segmentation operations on regions, so that the interpreted images and any proposed changes made to the regions can be made to conform to a valid histological model. Worked examples are given using images of H&E stained H400 cell line cultures.

show abstract

“…We discuss this further below by considering a set of possible topological segmentations satisfying an assumed histological model. While there is a large body of literature on evaluation methods (e.g., [15,16]), none of those frameworks adopt the topological framework assumed here, nor do they address systematic methods of resegmentation to bring the results of initial segmentation into line with the expectations of histological theory, which is the main focus of the present paper.…”

Section: Related Workmentioning

confidence: 99%

Mereotopological Correction of Segmentation Errors in Histological Imaging

et al. 2017

View full text Add to dashboard Cite

Abstract:In this paper we describe mereotopological methods to programmatically correct image segmentation errors, in particular those that fail to fulfil expected spatial relations in digitised histological scenes. The proposed approach exploits a spatial logic called discrete mereotopology to integrate a number of qualitative spatial reasoning and constraint satisfaction methods into imaging procedures. Eight mereotopological relations defined on binary region pairs are represented as nodes in a set of 20 directed graphs, where the node-to-node graph edges encode the possible transitions between the spatial relations after set-theoretic and discrete topological operations on the regions are applied. The graphs allow one to identify sequences of operations that applied to regions of a given relation, and enables one to resegment an image that fails to conform to a valid histological model into one that does. Examples of the methods are presented using images of H&E-stained human carcinoma cell line cultures.

show abstract

Gland segmentation in colon histology images: The glas challenge contest

Cited by 666 publications

References 49 publications

DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation

DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation

Model-Based Correction of Segmentation Errors in Digitised Histological Images

Mereotopological Correction of Segmentation Errors in Histological Imaging

Contact Info

Product

Resources

About