U-Net Using Stacked Dilated Convolutions for Medical Image Segmentation
This paper proposes a novel U-Net variant using stacked dilated convolutions for medical image segmentation (SDU-Net). SDU-Net adopts the architecture of vanilla U-Net with modifications in the encoder and decoder operations (an operation indicates all the processing for feature maps of the same resolution). Unlike vanilla U-Net which incorporates two standard convolutions in each encoder/decoder operation, SDU-Net uses one standard convolution followed by multiple dilated convolutions and concatenates all dilated convolution outputs as input to the next operation. Experiments showed that SDU-Net outperformed vanilla U-Net, attention U-Net (AttU-Net), and recurrent residual U-Net (R2U-Net) in all four tested segmentation tasks while using parameters around 40% of vanilla U-Net's, 17% of AttU-Net's, and 15% of R2U-Net's.
Code (0)
등록된 구현이 없습니다.
Tasks
AllDecoderImage SegmentationMedical Image SegmentationSegmentationSemantic SegmentationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Dilated Convolution with Dilated GRU for Music Source Separation
Stacked dilated convolutions used in Wavenet have been shown effective for generating high-quality audios. By replacing pooling/striding with dilation in convolution layers, they can preserve high-resolution information …
Music Source SeparationHC-Mamba: Vision MAMBA with Hybrid Convolutional Techniques for Medical Image Segmentation
Automatic medical image segmentation technology has the potential to expedite pathological diagnoses, thereby enhancing the efficiency of patient care. However, medical images often have complex textures and structures, …
Image SegmentationMambaMedical Image SegmentationOrgan Segmentation+2Dilated Convolutions in Neural Networks for Left Atrial Segmentation in 3D Gadolinium Enhanced-MRI
Segmentation of the left atrial chamber and assessing its morphology, are essential for improving our understanding of atrial fibrillation, the most common type of cardiac arrhythmia. Automation of this process in 3D gad…
Domain AdaptationImage SegmentationMedical Image SegmentationSegmentation+1Efficient Smoothing of Dilated Convolutions for Image Segmentation
Dilated Convolutions have been shown to be highly useful for the task of image segmentation. By introducing gaps into convolutional filters, they enable the use of larger receptive fields without increasing the original …
Image SegmentationSegmentationSemantic SegmentationD2Conv3D: Dynamic Dilated Convolutions for Object Segmentation in Videos
Despite receiving significant attention from the research community, the task of segmenting and tracking objects in monocular videos still has much room for improvement. Existing works have simultaneously justified the e…
Multi-Object Tracking and SegmentationSegmentationSemantic SegmentationUnsupervised Video Object Segmentation+4