3D Medical Imaging Segmentation on Non-Contrast CT
It provides a review for researchers in medical imaging, but is incremental as it synthesizes existing methods without new results.
This report analyzes non-contrast CT image segmentation, identifying nnUNet as the state-of-the-art method and discussing challenges like the long-tail problem and pre-training techniques.
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.