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BRB U-Net: Bottleneck Residual Blocks in U-Net for Light-Weight Semantic Segmentation
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 184 - Number 33 |
| Year of Publication: 2022 |
| Authors: Aruna Kumari Kakumani, L. Padma Sree |
10.5120/ijca2022922430
|
Aruna Kumari Kakumani, L. Padma Sree . BRB U-Net: Bottleneck Residual Blocks in U-Net for Light-Weight Semantic Segmentation. International Journal of Computer Applications. 184, 33 ( Oct 2022), 63-67. DOI=10.5120/ijca2022922430
Abstract
Cell is the fundamental entity of all living organisms. Understanding cell behaviour is improtant to study the biological processes in living organisms. In this work semantic segmentation of cells in microscopy images is studied. Specifically a novel deep learning architechture, BRB U-Net is proposed for the semantic segmentation of cells in microscopy. Bottleneck residual blocks are incorporated in U-Net architechture to achieve a light weight semantic segmentation model. The proposed method is evaluated with Phc-C2DH-U373 dataset of cell tracking challenge and achieves 0.9430 and 0.8383 dice similarity coefficient and intersection over union respectively. BRB U-Net achieved 7.68 times less number of parameters and model size is 7.35 times lesser than U-Net.
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