Multi-planar 2D-U-Net Segmentation of 3D-CT Abdominal Organs augmented by Spatial Occurrence Maps
arXiv:2606.07717v1 Announce Type: cross Abstract: This work proposes a lightweight 2D-U-Net-based framework for segmenting five abdominal organs in large field-of-view 3D CT scans. The method combines coarse-to-fine segmentation, predictions from multiple anatomical planes, and additional fuzzy 3D spatial maps that provide anatomical location cues to improve segmentation accuracy. We combine multi-planar 2D-U-Net models augmented by a spatial occurrence map. The approach involves two main stages. First, the abdominal volume of interest region is detected by traversing the whole scan axially wi
The continuous advancements in AI and medical imaging technology are enabling more sophisticated and accurate diagnostic tools for clinical applications.
Improved AI-driven medical image segmentation can lead to earlier and more precise diagnoses, better treatment planning, and potentially reduced healthcare costs.
The ability to accurately segment abdominal organs in 3D CT scans using lightweight 2D-U-Net models will enhance diagnostic efficiency and accessibility.
- · Medical AI companies
- · Radiologists
- · Healthcare providers
- · Patients
This technology directly improves the accuracy and speed of medical image analysis.
Wider adoption of such AI tools could lead to a decrease in misdiagnosis and an increase in early intervention for various conditions.
The integration of these AI models into clinical workflows may reshape the roles of diagnostic imaging specialists and accelerate drug discovery through better phenotyping.
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Read at arXiv cs.AI