Efficient deep learning architecture with dimension-wise pyramid pooling for nuclei segmentation of histopathology images

Anirudh Ashok Aatresh, Rohit Prashant Yatgiri, Amit Kumar Chanchal, Aman Kumar, Akansh Ravi, Devikalyan Das, Raghavendra BS, Shyam Lal, Jyoti Kini

Research output: Contribution to journalArticlepeer-review

Abstract

Image segmentation remains to be one of the most vital tasks in the area of computer vision and more so in the case of medical image processing. Image segmentation quality is the main metric that is often considered with memory and computation efficiency overlooked, limiting the use of power hungry models for practical use. In this paper, we propose a novel framework (Kidney-SegNet) that combines the effectiveness of an attention based encoder-decoder architecture with atrous spatial pyramid pooling with highly efficient dimension-wise convolutions. The segmentation results of the proposed Kidney-SegNet architecture have been shown to outperform existing state-of-the-art deep learning methods by evaluating them on two publicly available kidney and TNBC breast H&E stained histopathology image datasets. Further, our simulation experiments also reveal that the computational complexity and memory requirement of our proposed architecture is very efficient compared to existing deep learning state-of-the-art methods for the task of nuclei segmentation of H&E stained histopathology images. The source code of our implementation will be available at https://github.com/Aaatresh/Kidney-SegNet.

Original languageEnglish
Article number101975
JournalComputerized Medical Imaging and Graphics
Volume93
DOIs
Publication statusPublished - 10-2021

All Science Journal Classification (ASJC) codes

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design

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