TY - JOUR
T1 - Efficient deep learning architecture with dimension-wise pyramid pooling for nuclei segmentation of histopathology images
AU - Aatresh, Anirudh Ashok
AU - Yatgiri, Rohit Prashant
AU - Chanchal, Amit Kumar
AU - Kumar, Aman
AU - Ravi, Akansh
AU - Das, Devikalyan
AU - BS, Raghavendra
AU - Lal, Shyam
AU - Kini, Jyoti
N1 - Funding Information:
This research work was supported in part by the S cience Engineering and Research Board, Department of Science and Technology, Govt. of India under Grant No. EEG/2018/000323, 2019.
Funding Information:
This research work was supported in part by the Science Engineering and Research Board, Department of Science and Technology, Govt. of India under Grant No. EEG/2018/000323, 2019., Conflict of interest: None declared.
Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2021/10
Y1 - 2021/10
N2 - 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.
AB - 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.
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U2 - 10.1016/j.compmedimag.2021.101975
DO - 10.1016/j.compmedimag.2021.101975
M3 - Article
AN - SCOPUS:85113420457
SN - 0895-6111
VL - 93
JO - Computerized Medical Imaging and Graphics
JF - Computerized Medical Imaging and Graphics
M1 - 101975
ER -