ArticleFrontiers in genetics2021
MSU-Net: Multi-Scale U-Net for 2D Medical Image Segmentation.
Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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40 citing papers in PubMed, 155 citations in OpenAlex.
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- GLAC-Unet: Global-Local Active Contour Loss with an Efficient U-Shaped Architecture for Multiclass Medical Image Segmentation.Journal of imaging informatics in medicine · 2025Article
- DCM-Net: dual-encoder CNN-Mamba network with cross-branch fusion for robust medical image segmentation.BMC medical imaging · 2025Article
- A Neural Conditional Random Field Model Using Deep Features and Learnable Functions for End-to-End MRI Prostate Zonal Segmentation.The journal of machine learning for biomedical imaging · 2025Article
- Image-based identification and isolation of micronucleated cells to dissect cellular consequences.eLife · 2025Article
- EFCNet enhances the efficiency of segmenting clinically significant small medical objects.Scientific reports · 2025Article
- A semi-supervised domain adaptation method with scale-aware and global-local fusion for abdominal multi-organ segmentation.Journal of applied clinical medical physics · 2025Article
- CapNet: An Automatic Attention-Based with Mixer Model for Cardiovascular Magnetic Resonance Image Segmentation.Journal of imaging informatics in medicine · 2025Article
- Enhancing signal-to-noise ratio in real-time LED-based photoacoustic imaging: A comparative study of CNN-based deep learning architectures.Photoacoustics · 2025Article
- Leveraging multi-scale feature integration in UNet and FPN for semantic segmentation of lung nodules.Frontiers in artificial intelligence · 2025Article
- Refining CT image analysis: Exploring adaptive fusion in U-nets for enhanced brain tissue segmentation.PloS one · 2025Article
- Article
- EF-net: Accurate edge segmentation for segmenting COVID-19 lung infections from CT images.Heliyon · 2024Article
- A review of deep learning-based reconstruction methods for accelerated MRI using spatiotemporal and multi-contrast redundancies.Biomedical engineering letters · 2024Review
- Conditional Diffusion Model with Spatial Attention and Latent Embedding for Medical Image Segmentation.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- Next-Gen Medical Imaging: U-Net Evolution and the Rise of Transformers.Sensors (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aiming at the limitation of the convolution kernel with a fixed receptive field and unknown prior to optimal network width in U-Net, multi-scale U-Net (MSU-Net) is proposed by us for medical image segmentation. First, multiple convolution sequence is used to extract more semantic features from the images. Second, the convolution kernel with different receptive fields is used to make features more diverse. The problem of unknown network width is alleviated by efficient integration of convolution kernel with different receptive fields. In addition, the multi-scale block is extended to other variants of the original U-Net to verify its universality. Five different medical image segmentation datasets are used to evaluate MSU-Net. A variety of imaging modalities are included in these datasets, such as electron microscopy, dermoscope, ultrasound, etc. Intersection over Union (IoU) of MSU-Net on each dataset are 0.771, 0.867, 0.708, 0.900, and 0.702, respectively. Experimental results show that MSU-Net achieves the best performance on different datasets. Our implementation is available at https://github.com/CN-zdy/MSU_Net.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.