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Gland segmentation in colon histology images: The glas challenge contest

Korsuk Sirinukunwattana, Josien P.W. Pluim, Hao Chen, Xiaojuan Qi, Pheng-Ann Heng, Yun Bo Guo, Li Yang Wang, Bogdan J. Matuszewski, Elia Bruni, Urko Sanchez, Anton Böhm, Olaf Ronneberger (+7 others)
2017 Medical Image Analysis  
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]

Korsuk Sirinukunwattana, Josien P. W. Pluim, Hao Chen, Xiaojuan Qi, Pheng-Ann Heng, Yun Bo Guo, Li Yang Wang, Bogdan J. Matuszewski, Elia Bruni, Urko Sanchez, Anton Böhm, Olaf Ronneberger, Bassem Ben Cheikh, Daniel Racoceanu (+5 others)
2016 arXiv   pre-print
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

Wenqi Li, Siyamalan Manivannan, Shazia Akbar, Jianguo Zhang, Emanuele Trucco, Stephen J. McKenna
2016 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)  
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

Saima Rathore, Muhammad Aksam Iftikhar, Ahmad Chaddad, Tamim Niazi, Thomas Karasic, Michel Bilello
2019 Cancers  
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]

A. Khvostikov, A. Krylov, O. Kharlova, N. Oleynikova, I. Mikhailov, P. Malkov
2018 arXiv   pre-print
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

Shan E Ahmed Raza, Linda Cheung, Muhammad Shaban, Simon Graham, David Epstein, Stella Pelengaris, Michael Khan, Nasir M. Rajpoot
2019 Medical Image Analysis  
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

Paola Sena, Rita Fioresi, Francesco Faglioni, Lorena Losi, Giovanni Faglioni, Luca Roncucci
2019 Oncology Letters  
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]

Soufiane Belharbi, Marco Pedersoli, Ismail Ben Ayed, Luke McCaffrey, Eric Granger
2022 arXiv   pre-print
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

Yves-Rémi Van Eycke, Cédric Balsat, Laurine Verset, Olivier Debeir, Isabelle Salmon, Christine Decaestecker
2018 Medical Image Analysis  
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

Siyamalan Manivannan, Wenqi Li, Jianguo Zhang, Emanuele Trucco, Stephen J. McKenna
2018 IEEE Transactions on Medical Imaging  
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]

Soufiane Belharbi, Jérôme Rony, Jose Dolz, Ismail Ben Ayed, Luke McCaffrey, Eric Granger
2021 arXiv   pre-print
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

Korsuk Sirinukunwattana, David R. J. Snead, Nasir M. Rajpoot
2015 IEEE Transactions on Medical Imaging  
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

Philipp Kainz, Michael Pfeiffer, Martin Urschler
2017 PeerJ  
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]

Jérôme Rony, Soufiane Belharbi, Jose Dolz, Ismail Ben Ayed, Luke McCaffrey, Eric Granger
2022 arXiv   pre-print
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]

Bingzhi Chen, Yishu Liu, Zheng Zhang, Guangming Lu, David Zhang
2021 arXiv   pre-print
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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