ArticleDiagnostics (Basel, Switzerland)2020
SD-UNet: Stripping Down U-Net for Segmentation of Biomedical Images on Platforms with Low Computational Budgets.
Article in Diagnostics (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
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- Mu-Net a Light Architecture for Small Dataset Segmentation of Brain Organoid Bright-Field Images.Biomedicines · 2023Article
- LcmUNet: A Lightweight Network Combining CNN and MLP for Real-Time Medical Image Segmentation.Bioengineering (Basel, Switzerland) · 2023Article
- Hexagonal-Grid-Layout Image Segmentation Using Shock Filters: Computational Complexity Case Study for Microarray Image Analysis Related to Machine Learning Approaches.Sensors (Basel, Switzerland) · 2023Article
- AID-U-Net: An Innovative Deep Convolutional Architecture for Semantic Segmentation of Biomedical Images.Diagnostics (Basel, Switzerland) · 2022Article
- Weighted average ensemble-based semantic segmentation in biological electron microscopy images.Histochemistry and cell biology · 2022Article
- An interpretable and versatile machine learning approach for oocyte phenotyping.Journal of cell science · 2022Article
- Use of U-Net Convolutional Neural Networks for Automated Segmentation of Fecal Material for Objective Evaluation of Bowel Preparation Quality in Colonoscopy.Diagnostics (Basel, Switzerland) · 2022Article
- Human-Computer Interaction with Hand Gesture Recognition Using ResNet and MobileNet.Computational intelligence and neuroscience · 2022Article
- Semantic Segmentation of Urinary Bladder Cancer Masses from CT Images: A Transfer Learning Approach.Biology · 2021Article
- Esophagus Segmentation in CT Images via Spatial Attention Network and STAPLE Algorithm.Sensors (Basel, Switzerland) · 2021Article
- In-Series U-Net Network to 3D Tumor Image Reconstruction for Liver Hepatocellular Carcinoma Recognition.Diagnostics (Basel, Switzerland) · 2020Article
- Automatic Identification of Down Syndrome Using Facial Images with Deep Convolutional Neural Network.Diagnostics (Basel, Switzerland) · 2020Article
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Authors and funding
7 authors.
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Abstract
During image segmentation tasks in computer vision, achieving high accuracy performance while requiring fewer computations and faster inference is a big challenge. This is especially important in medical imaging tasks but one metric is usually compromised for the other. To address this problem, this paper presents an extremely fast, small and computationally effective deep neural network called Stripped-Down UNet (SD-UNet), designed for the segmentation of biomedical data on devices with limited computational resources. By making use of depthwise separable convolutions in the entire network, we design a lightweight deep convolutional neural network architecture inspired by the widely adapted U-Net model. In order to recover the expected performance degradation in the process, we introduce a weight standardization algorithm with the group normalization method. We demonstrate that SD-UNet has three major advantages including: (i) smaller model size (23x smaller than U-Net); (ii) 8x fewer parameters; and (iii) faster inference time with a computational complexity lower than 8M floating point operations (FLOPs). Experiments on the benchmark dataset of the Internatioanl Symposium on Biomedical Imaging (ISBI) challenge for segmentation of neuronal structures in electron microscopic (EM) stacks and the Medical Segmentation Decathlon (MSD) challenge brain tumor segmentation (BRATs) dataset show that the proposed model achieves comparable and sometimes better results compared to the current state-of-the-art.
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