mlsanityData to 14 Oct 2026

3D Medical Imaging Segmentation on Non-Contrast CT

arXiv 2503.08361 ↗First posted 11 Mar 2025eess.IV cs.CVcited by 0

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Abstract (arXiv)

This technical report analyzes non-contrast CT image segmentation in computer vision. It revisits a proposed method, examines the background of non-contrast CT imaging, and highlights the significance of segmentation. The study reviews representative methods, including convolutional-based and CNN-Transformer hybrid approaches, discussing their contributions, advantages, and limitations. The nnUNet stands out as the state-of-the-art method across various segmentation tasks. The report explores the relationship between the proposed method and existing approaches, emphasizing the role of global context modeling in semantic labeling and mask generation. Future directions include addressing the long-tail problem, utilizing pre-trained models for medical imaging, and exploring self-supervised or contrastive pre-training techniques. This report offers insights into non-contrast CT image segmentation and potential advancements in the field.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 44 of its 85 references that are in the map. First posted on arXiv 11 Mar 2025.

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44 of 85 references.

References · 44

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32 more
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