190 research outputs found
3D Matting: A Soft Segmentation Method Applied in Computed Tomography
Three-dimensional (3D) images, such as CT, MRI, and PET, are common in
medical imaging applications and important in clinical diagnosis. Semantic
ambiguity is a typical feature of many medical image labels. It can be caused
by many factors, such as the imaging properties, pathological anatomy, and the
weak representation of the binary masks, which brings challenges to accurate 3D
segmentation. In 2D medical images, using soft masks instead of binary masks
generated by image matting to characterize lesions can provide rich semantic
information, describe the structural characteristics of lesions more
comprehensively, and thus benefit the subsequent diagnoses and analyses. In
this work, we introduce image matting into the 3D scenes to describe the
lesions in 3D medical images. The study of image matting in 3D modality is
limited, and there is no high-quality annotated dataset related to 3D matting,
therefore slowing down the development of data-driven deep-learning-based
methods. To address this issue, we constructed the first 3D medical matting
dataset and convincingly verified the validity of the dataset through quality
control and downstream experiments in lung nodules classification. We then
adapt the four selected state-of-the-art 2D image matting algorithms to 3D
scenes and further customize the methods for CT images. Also, we propose the
first end-to-end deep 3D matting network and implement a solid 3D medical image
matting benchmark, which will be released to encourage further research.Comment: 12 pages, 7 figure
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