Gland segmentation in colorectal cancer histopathological images using U-net inspired convolutional network

Rastogi, Priyanka; Khanna, Kavita; Singh, Vijendra

doi:10.1007/s00521-021-06687-z

Cited by 28 publications

(1 citation statement)

References 18 publications

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“…on the same dataset 13 and is comparable to that of existing literature. 39 – 41 However, it is important to acknowledge that the mean Hausdorff distances for all algorithms overlap significantly within these uncertainties. This overlap indicates that, although TimeSformer has the best average performance, it may not consistently outperform the other algorithms in every instance.…”

Section: Discussionmentioning

confidence: 99%

Detection and margin assessment of thyroid carcinoma with microscopic hyperspectral imaging using transformer networks

Tran,

Ma,

Mubarak

et al. 2024

J. Biomed. Opt.

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. Significance Hyperspectral imaging (HSI) is an emerging imaging modality for oncological applications and can improve cancer detection with digital pathology. Aim The study aims to highlight the increased accuracy and sensitivity of detecting the margin of thyroid carcinoma in hematoxylin and eosin (H&E)-stained histological slides using HSI and data augmentation methods. Approach Using an automated microscopic imaging system, we captured 2599 hyperspectral images from 65 H&E-stained human thyroid slides. Images were then preprocessed into 153,906 image patches of dimension . We modified the TimeSformer network architecture, which used alternating spectral attention and spatial attention layers. We implemented several data augmentation methods for HSI based on the RandAugment algorithm. We compared the performances of TimeSformer on HSI against the performances of pretrained ConvNext and pretrained vision transformers (ViT) networks on red, green, and blue (RGB) images. Finally, we applied attention unrolling techniques on the trained TimeSformer network to identify the biological features to which the network paid attention. Results In the testing dataset, TimeSformer achieved an accuracy of 90.87%, a weighted score of 89.79%, a sensitivity of 91.50%, and an area under the receiving operator characteristic curve (AU-ROC) score of 97.04%. Additionally, TimeSformer produced thyroid carcinoma tumor margins with an average Jaccard score of 0.76 mm. Without data augmentation, TimeSformer achieved an accuracy of 88.23%, a weighted score of 86.46%, a sensitivity of 85.53%, and an AU-ROC score of 94.94%. In comparison, the ViT network achieved an 89.98% accuracy, an 88.14% weighted score, an 84.77% sensitivity, and a 96.17% AU-ROC. Our visualization results showed that the network paid attention to biological features. Conclusions The TimeSformer model trained with hyperspectral histological data consistently outperformed conventional RGB-based models, highlighting the superiority of HSI in this context. Our proposed augmentation methods improved the accuracy, the score, and the sensitivity score.

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Section: Discussionmentioning

confidence: 99%

Detection and margin assessment of thyroid carcinoma with microscopic hyperspectral imaging using transformer networks

Tran,

Ma,

Mubarak

et al. 2024

J. Biomed. Opt.

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Revolutionizing Colon Histopathology Glandular Segmentation Using an Ensemble Network With Watershed Algorithm

Roy,

Gupta,

Goswami

2024

Int J Imaging Syst Tech

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Colorectal adenocarcinoma, the most prevalent form of colon cancer, originates in the glandular structures of the intestines, presenting histopathological abnormalities in affected tissues. Accurate gland segmentation is crucial for identifying these potentially fatal abnormalities. While recent methodologies have shown success in segmenting glands in benign tissues, their efficacy diminishes when applied to malignant tissue segmentation. This study aims to develop a robust learning algorithm using a convolutional neural network (CNN) to segment glandular structures in colon histology images. The methodology employs a CNN based on the U‐Net architecture, augmented by a weighted ensemble network that integrates DenseNet 169, Inception V3, and Efficientnet B3 as backbone models. Additionally, the segmented gland boundaries are refined using the watershed algorithm. Evaluation on the Warwick‐QU dataset demonstrates promising results for the ensemble model, by achieving an F1 score of 0.928 and 0.913, object dice coefficient of 0.923 and 0.911, and Hausdorff distances of 38.97 and 33.76 on test sets A and B, respectively. These results are compared with outcomes from the GlaS challenge (MICCAI 2015) and existing research findings. Furthermore, our model is validated with a publicly available dataset named LC25000, and visual inspection reveals promising results, further validating the efficacy of our approach. The proposed ensemble methodology underscores the advantages of amalgamating diverse models, highlighting the potential of ensemble techniques to enhance segmentation tasks beyond individual model capabilities.

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An improved Hover-net for nuclear segmentation and classification in histopathology images

Wang

Qin

Chen

et al. 2023

Neural Comput & Applic

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Gland segmentation in colorectal cancer histopathological images using U-net inspired convolutional network

Cited by 28 publications

References 18 publications

Detection and margin assessment of thyroid carcinoma with microscopic hyperspectral imaging using transformer networks

Detection and margin assessment of thyroid carcinoma with microscopic hyperspectral imaging using transformer networks

Revolutionizing Colon Histopathology Glandular Segmentation Using an Ensemble Network With Watershed Algorithm

An improved Hover-net for nuclear segmentation and classification in histopathology images

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