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

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Abstract

The accurate gland segmentation from digitized H&E (hematoxylin and eosin) histology images with a wide range of histologic grades of cancer is quite challenging. The methodologies proposed in recent researches have performed well in segmenting glands from benign subjects but have not given satisfactory results when segmenting glands from malignant cases. The methodology proposed in this paper is based on the symmetric encoder-decoder network which works remarkably well in detecting and segmenting glands in the case of malignant subjects and overall. The proposed pipelines harness the power of multilevel CNN architecture to capture contextual information and concatenation of features from skip connections with upsampled feature maps to improve the localization accuracy thereby improving the precise segmentation accuracy. The raw predicted map is further refined using morphological operators as a post-processing tool. The method is trained and evaluated on the Warwick-QU dataset. The final segmentation results have been compared with the performance of top-performing teams of gland challenge (GlaS) MICCI 2015 along with the recent researches. The final predicted segmentation maps have achieved the F1 score of 0.81 for gland detection, object dice score of 0.82 for segmentation, and Hausdorff distance of 84.18 for gland shape similarity which is akin to or higher than the existing models for the malignant subject. The model has done affably well on the overall dataset too.

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Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Computer Science Engineering, The NorthCap University, Gurugram, India

    Priyanka Rastogi & Kavita Khanna

  2. School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India

    Vijendra Singh

Authors
  1. Priyanka Rastogi
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  2. Kavita Khanna
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  3. Vijendra Singh
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Contributions

All authors contributed to the study's conception and design. Material preparation, data collection, and analysis were performed by Priyanka Rastogi. The first draft of the manuscript was written by Priyanka Rastogi and all authors commented on previous versions of the manuscript. All authors have read and approved the final manuscript.

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Correspondence to Priyanka Rastogi.

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The authors have no conflicts of interest to declare that are relevant to the content of this article.

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The article does not contain any studies with human participants or animals performed by any of the authors.

Data availability

The datasets used in conducting the study are available on the link https://warwick.ac.uk/fac/cross_fac/tia/data/glascontest/download while submitting the manuscript.

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https://github.com/priyankarast/GlandSegmentation.

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Rastogi, P., Khanna, K. & Singh, V. Gland segmentation in colorectal cancer histopathological images using U-net inspired convolutional network. Neural Comput & Applic 34, 5383–5395 (2022). https://doi.org/10.1007/s00521-021-06687-z

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  • DOI: https://doi.org/10.1007/s00521-021-06687-z

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