CMU-Net: A Strong ConvMixer-based Medical Ultrasound Image Segmentation Network
U-Net and its extensions have achieved great success in medical image segmentation. However, due to the inherent local characteristics of ordinary convolution operations, U-Net encoder cannot effectively extract global context information. In addition, simple skip connections cannot capture salient features. In this work, we propose a fully convolutional segmentation network (CMU-Net) which incorporates hybrid convolutions and multi-scale attention gate. The ConvMixer module extracts global context information by mixing features at distant spatial locations. Moreover, the multi-scale attention gate emphasizes valuable features and achieves efficient skip connections. We evaluate the proposed method using both breast ultrasound datasets and a thyroid ultrasound image dataset; and CMU-Net achieves average Intersection over Union (IoU) values of 73.27% and 84.75%, and F1 scores of 84.81% and 91.71%. The code is available at https://github.com/FengheTan9/CMU-Net.
Code (2)
Tasks
Image SegmentationMedical Image SegmentationSegmentationTumor SegmentationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Deep Learning Architectures for Diagnosis of Diabetic Retinopathy
For many years, convolutional neural networks dominated the field of computer vision, not least in the medical field, where problems such as image segmentation were addressed by such networks as the U-Net. The arrival of…
Deep LearningImage SegmentationRetinal Vessel SegmentationSegmentation+1Ultrasound SAM Adapter: Adapting SAM for Breast Lesion Segmentation in Ultrasound Images
Segment Anything Model (SAM) has recently achieved amazing results in the field of natural image segmentation. However, it is not effective for medical image segmentation, owing to the large domain gap between natural an…
Image SegmentationLesion SegmentationMedical Image SegmentationSegmentation+1Deep Learning in Medical Ultrasound Image Segmentation: a Review
Applying machine learning technologies, especially deep learning, into medical image segmentation is being widely studied because of its state-of-the-art performance and results. It can be a key step to provide a reliabl…
3D ReconstructionDeep LearningImage SegmentationMedical Image Segmentation+2Semantic Segmentation Refiner for Ultrasound Applications with Zero-Shot Foundation Models
Despite the remarkable success of deep learning in medical imaging analysis, medical image segmentation remains challenging due to the scarcity of high-quality labeled images for supervision. Further, the significant dom…
Image SegmentationMedical Image SegmentationSegmentationSemantic SegmentationA simple thinking about the application of the attention mechanism in medical ultrasound image segmentation task
The AI-based assisted diagnosis programs have been widely investigated on medical ultrasound images. Complex scenario of ultrasound image, in which the coupled interference of internal and external factors is severe, bri…
Image SegmentationSemantic Segmentation