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Gland segmentation in colon histology images: The glas challenge contest
2017
Medical Image Analysis
Preserved Fulltext
This paper provides an overview to the Gland Segmentation in Colon Histology Images Challenge Contest (GlaS) held at MICCAI'2015. ...
This was a main reason for organizing this challenge 40 contest. 41 The Gland Segmentation in Colon Histology Images (GlaS) challenge 1 42 brought together computer vision and medical image computing researchers ...
as well as bring 745 precision and accuracy into assessment and prediction of the outcome of the presented a summary of the Gland Segmentation in Colon 749 Histology Images (GlaS) Challenge Contest which ...
doi:10.1016/j.media.2016.08.008
pmid:27614792
fatcat:dfeegemgzbaxjko34l73cptbve
Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest
[article]
2016
arXiv
pre-print
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2016 pre-print arXiv:1603.00275v1 fatcat:bjxebpvqdnfs5hp7tmwixum7bq
This paper provides an overview to the Gland Segmentation in Colon Histology Images Challenge Contest (GlaS) held at MICCAI'2015. ...
Colorectal adenocarcinoma originating in intestinal glandular structures is the most common form of colon cancer. ...
Conclusions This paper presented a summary of the Gland Segmentation in Colon Histology Images (GlaS) Challenge Contest which was held in conjunction with the 18th International Conference on Medical Image ...
arXiv:1603.00275v2
fatcat:uw367nsyhrhohkymtqf3lh2ioi
Gland segmentation in colon histology images using hand-crafted features and convolutional neural networks
2016
2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)
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We investigate glandular structure segmentation in colon histology images as a window-based classification problem. ...
On 85 images of H&E-stained tissue, we find that fine-tuned CNN outperforms HC-SVM in gland segmentation measured by pixel-wise Jaccard and Dice indices. ...
INTRODUCTION Analysis of gland structures is an important component of histopathological examinations. In this paper we address the challenging problem of gland segmentation in histology images. ...
doi:10.1109/isbi.2016.7493530
dblp:conf/isbi/LiMAZTM16
fatcat:2cnohhllqngopog7fwpstek6nq
Segmentation and Grade Prediction of Colon Cancer Digital Pathology Images Across Multiple Institutions
2019
Cancers
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Distinguishing benign from malignant disease is a primary challenge for colon histopathologists. ...
To provide more reliable analysis of colon specimens, we propose an end-to-end computational pathology pipeline that encompasses gland segmentation, cancer detection, and then further breaking down the ...
Conflicts of Interest: The authors declare no conflict of interests. ...
doi:10.3390/cancers11111700
pmid:31683818
pmcid:PMC6896042
fatcat:6jmwoerbfjg4hljsmqmxwv5oo4
Histological images segmentation of mucous glands
[article]
2018
arXiv
pre-print
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We review major trends in histological images segmentation and design a new convolutional neural network for mucous gland segmentation. ...
Accurate segmentation of mucous glands from histology images is a crucial step to obtain reliable morphometric criteria for quantitative diagnostic methods. ...
Warwick-QU dataset was also used in the Gland Segmentation in Colon Histology Images (GlaS) contest [16] . ...
arXiv:1806.07781v1
fatcat:bifrfmermrfgdklziql5cqunl4
Micro-Net: A unified model for segmentation of various objects in microscopy images
2019
Medical Image Analysis
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The proposed network can be used to segment cells, nuclei and glands in fluorescence microscopy and histology images after slight tuning of input parameters. ...
Object segmentation and structure localization are important steps in automated image analysis pipelines for microscopy images. ...
Sample images of mouse pancreatic exocrine cells and endocrine cells are
Gland Segmentation (GLaS) Challenge Data Set
190 Histological assessment of glands is one of the key factors in colon cancer ...
doi:10.1016/j.media.2018.12.003
pmid:30580111
fatcat:7wg3w4zqx5ct7o3yzrqu3ktkhu
Deep learning techniques for detecting preneoplastic and neoplastic lesions in human colorectal histological images
2019
Oncology Letters
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The approach used in the present study is 'direct'; it labels raw images and bypasses the segmentation step. ...
The present study demonstrated that the neural network, when properly trained, can provide fast, accurate and reproducible labeling for colon cancer images, with the potential to significantly improve ...
Marie Slodova Curie Action funded the current study via the GHAIA Geometric Harmonic Analysis for Intersciplinary Application (grant no. GA 777822). ...
doi:10.3892/ol.2019.10928
pmid:31788084
pmcid:PMC6865164
fatcat:y7kyhprjyjeyrfa3h276f5od7q
Negative Evidence Matters in Interpretable Histology Image Classification
[article]
2022
arXiv
pre-print
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This problem is known to be more challenging with histology images than with natural ones, since objects are less salient, structures have more variations, and foreground and background regions have stronger ...
Extensive experiments show that our proposed method can substantial outperform related state-of-art methods on GlaS (public benchmark for colon cancer), and Camelyon16 (patch-based benchmark for breast ...
The Gland Segmentation in Colon Histology Contest: https://warwick.ac.uk/fac/sci/dcs/research/tia/glascontest ...
arXiv:2201.02445v3
fatcat:um5zhhxvzjgmdipdyolcz7xiau
Segmentation of glandular epithelium in colorectal tumours to automatically compartmentalise IHC biomarker quantification: A deep learning approach
2018
Medical Image Analysis
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Our method achieves better performances than the state of the art on the H&E images of the GlaS challenge contest, whereas it uses only the haematoxylin colour channel extracted by colour deconvolution ...
Our objective is to segment glandular epithelium in histological images from tissue slides submitted to different staining techniques, including usual haematoxylin-eosin (H&E) as well as immunohistochemistry ...
U-Net from Ronneberger et al. (2015) , which was proven efficient on other medical image segmentation problems, also yielded very good results in the GlaS Challenge Contest. ...
doi:10.1016/j.media.2018.07.004
pmid:30081241
fatcat:hyelukj4dvhzzmdidbc3t2sc3y
Structure Prediction for Gland Segmentation With Hand-Crafted and Deep Convolutional Features
2018
IEEE Transactions on Medical Imaging
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Using the GlaS contest protocol, our method achieves the overall best performance. ...
We present a novel method to segment instances of glandular structures from colon histopathology images. ...
It formed the basis of the Gland Segmentation (GlaS) Challenge Contest hosted by MICCAI [2] and is now publicly available 1 . ...
doi:10.1109/tmi.2017.2750210
pmid:28910760
fatcat:44tmjrrxuza6vdqhhixfa4pnou
Deep Interpretable Classification and Weakly-Supervised Segmentation of Histology Images via Max-Min Uncertainty
[article]
2021
arXiv
pre-print
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in segmentations, as is the case in challenging histology images. ...
Comprehensive experiments and ablation studies over the public GlaS colon cancer data and a Camelyon16 patch-based benchmark for breast cancer show substantial improvements over state-of-the-art WSL methods ...
Acknowledgment This research was supported in part by the Canadian Institutes of Health Research, the Natural Sciences and Engineering Research Council of Canada, and Compute Canada. ...
arXiv:2011.07221v3
fatcat:ufvonnrvnbakjlgckjbymp2kqa
A Stochastic Polygons Model for Glandular Structures in Colon Histology Images
2015
IEEE Transactions on Medical Imaging
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At the time of publication, this dataset is being released as part of the Gland Segmentation (GlaS) challenge contest to be held in conjunction with MICCAI 2015.
B. ...
Fig. 3 . 3 The Random Polygons Model framework. (a) A sample Hematoxylin and Eosin stained colon histology image. (b) A glandular probability map. ...
doi:10.1109/tmi.2015.2433900
pmid:25993703
fatcat:pivzllq7urfxjhvm3dibndqaye
Segmentation and classification of colon glands with deep convolutional neural networks and total variation regularization
2017
PeerJ
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MICCAI2015 colon gland segmentation challenge. ...
We present both quantitative and qualitative segmentation results on the recently released and publicly available Warwick-QU colon adenocarcinoma dataset associated with the GlaS@MICCAI2015 challenge and ...
ACKNOWLEDGEMENTS The authors are grateful to the organizers of the GlaS@MICCAI2015 challenge for providing (i) the Warwick-QU image dataset, and (ii) the MATLAB evaluation scripts for computing performance ...
doi:10.7717/peerj.3874
pmid:29018612
pmcid:PMC5629961
fatcat:riresjj6h5hd3hubccao57kgii
Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A Comparative Study
[article]
2022
arXiv
pre-print
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Four key challenges are identified for the application of deep WSOL methods in histology -- under/over activation of CAMs, sensitivity to thresholding, and model selection. ...
Cancer grading and localization of regions of interest (ROIs) in these images normally relies on both image- and pixel-level labels, the latter requiring a costly annotation process. ...
This protocol generates a benchmark containing a total of 48,870 samples: 24,348 samples for training, 8,858 samples for val- 5 The Gland Segmentation in Colon Histology Contest: https://warwick.ac.uk/ ...
arXiv:1909.03354v5
fatcat:cbkan6dnl5ctdankkv4tblso6e
TransAttUnet: Multi-level Attention-guided U-Net with Transformer for Medical Image Segmentation
[article]
2021
arXiv
pre-print
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To overcome the above challenges, this paper proposes a novel Transformer based medical image semantic segmentation framework called TransAttUnet, in which the multi-level guided attention and multi-scale ...
With the development of deep encoder-decoder architectures and large-scale annotated medical datasets, great progress has been achieved in the development of automatic medical image segmentation. ...
the Colon Histology Images Challenge Contest of MICCAl'2015 that aims to improve an automated approach which quantifies the morphology of glands. ...
arXiv:2107.05274v1
fatcat:22plkxgifvg5llpsaopgbe5ir4
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