Automatic Mucous Glands Segmentation in Histological Images

Khvostikov, Alexander; Krylov, Andrey S.; Михайлов, И. А.; Kharlova, O A; Oleynikova, N A; Мальков, П.Г.

doi:10.5194/isprs-archives-xlii-2-w12-103-2019

Cited by 11 publications

(9 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the gland segmentation category, 25 models were found, and all the models used 165 images of the Warwick QU dataset from the Gland Segmentation in Colon Histology Images (GlaS) challenge in 2015 ( Table 1 ) [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ]. The GlaS challenge was a sub-event of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) held to solve the problem of gland segmentation [ 50 ].…”

Section: Resultsmentioning

confidence: 99%

“…Overall, the teams showed F1-scores up to 0.91, object Dice scores up to 0.89, and object Hausdorff scores up to 45.4 in the offsite dataset (Part A) and similar results in the onsite dataset (Part B) ( Table 1 ) [ 21 , 22 ]. All the studies after the GlaS challenge also used the same dataset and showed better performance: F1-scores up to 0.92, object Dice scores up to 0.91, and object Hausdorff scores up to 39.8 for Part A as well as Part B ( Table 1 ) [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ].…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Current Trends of Artificial Intelligence for Colorectal Cancer Pathology Image Analysis: A Systematic Review

Thakur

Yoon²,

Chong

2020

Cancers

View full text Add to dashboard Cite

Colorectal cancer (CRC) is one of the most common cancers requiring early pathologic diagnosis using colonoscopy biopsy samples. Recently, artificial intelligence (AI) has made significant progress and shown promising results in the field of medicine despite several limitations. We performed a systematic review of AI use in CRC pathology image analysis to visualize the state-of-the-art. Studies published between January 2000 and January 2020 were searched in major online databases including MEDLINE (PubMed, Cochrane Library, and EMBASE). Query terms included “colorectal neoplasm,” “histology,” and “artificial intelligence.” Of 9000 identified studies, only 30 studies consisting of 40 models were selected for review. The algorithm features of the models were gland segmentation (n = 25, 62%), tumor classification (n = 8, 20%), tumor microenvironment characterization (n = 4, 10%), and prognosis prediction (n = 3, 8%). Only 20 gland segmentation models met the criteria for quantitative analysis, and the model proposed by Ding et al. (2019) performed the best. Studies with other features were in the elementary stage, although most showed impressive results. Overall, the state-of-the-art is promising for CRC pathological analysis. However, datasets in most studies had relatively limited scale and quality for clinical application of this technique. Future studies with larger datasets and high-quality annotations are required for routine practice-level validation.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Current Trends of Artificial Intelligence for Colorectal Cancer Pathology Image Analysis: A Systematic Review

Thakur

Yoon²,

Chong

2020

Cancers

View full text Add to dashboard Cite

show abstract

“…The continuation of this research will include enhancing the prediction of glands contours maps and evaluating the method on PATH-DT-MSU [16,17] dataset.…”

Section: Discussionmentioning

confidence: 99%

Adaptive Method of Glands Segmentation on Histological Images

Kosov

Khvostikov

Krylov

2020

Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2

View full text Add to dashboard Cite

Glands segmentation is a very important and yet challenging problem of histological images analysis. Accurate segmentation of mucous glands is a crucial step to obtain reliable morphological statistics and is necessary for the development of high-quality diagnostic algorithms, which are an integral part of timely medical care. In this paper we propose a two-stage segmentation method, which predicts the probability maps of glands boundaries in histological images based on a priori knowledge about the geometric shape of the mucous glands and uses a convolutional neural network (CNN) model to get the final segmentation result based on the predicted probability maps. The proposed method demonstrates good results in separating adjacent glands, which is one of the most challenging aspects in automatic segmentation of histological glands and one of the most complicated for algorithms based on applying convolutional neural networks. The evaluation of the proposed algorithm was performed with Warwick-QU dataset, which contains real histological images of colon tissue.

show abstract

“…The third dataset is the subset of PATH-DT-MSU dataset [6,7], containing 7 H&E whole slide histological images of digestive tract tumors. Each WSI image is a full-thickness fragment of the stomach wall, cut from the surgical material, and includes areas of adenocarcinoma, adjacent areas of visually unchanged lamina propria and the underlying layers of the stomach wall (muscularis mucosae, submucosa, own muscle layer, subserous areas).…”

Section: Used Datamentioning

confidence: 99%

Tissue Type Recognition in Whole Slide Histological Images

Khvostikov

Krylov

Михайлов

et al. 2021

Proceedings of the 31th International Conference on Computer Graphics and Vision. Volume 2

View full text Add to dashboard Cite

Automatic layers recognition of the wall of the stomach and colon on whole slide images is an extremely urgent task in digital pathology as it can be used for automatic determining the depth of invasion of the digestive tract tumors. In this paper we propose a new CNN-based method of automatic tissue type recognition on whole slide histological images. We also describe an effective pipeline of training that uses 2 different training datasets. The proposed method of automatic tissue type recognition achieved 0.929 accuracy and 0.903 balanced accuracy on CRC-VAL-HE-7K dataset for 9-types classification and 0.98 accuracy and 0.926 balanced accuracy on the test subset of whole slide images from PATH-DT- MSU dataset for 5-types classification. The developed method makes it possible to classify the areas corresponding to the gastric own mucous glands in the lamina propria and also to distinguish the tubular structures of a highly differentiated gastric adenocarcinoma with normal glands.

show abstract

Automatic Mucous Glands Segmentation in Histological Images

Cited by 11 publications

References 18 publications

Current Trends of Artificial Intelligence for Colorectal Cancer Pathology Image Analysis: A Systematic Review

Current Trends of Artificial Intelligence for Colorectal Cancer Pathology Image Analysis: A Systematic Review

Adaptive Method of Glands Segmentation on Histological Images

Tissue Type Recognition in Whole Slide Histological Images

Contact Info

Product

Resources

About