Distilling Temporal Coherence into 2D Networks for Transrectal Ultrasound Prostate Video Segmentation
arXiv:2606.31198v1 Announce Type: cross Abstract: Real-time video segmentation of the prostate in Transrectal Ultrasound (TRUS) is essential for image-guided interventions. While conventional 2D methods suffer from inter-frame inconsistencies by disregarding temporal context, 3D architectures incur prohibitive latency. To resolve this dilemma, we present a Temporally Consistent Learning Framework that distills temporal coherence into a 2D network during training, preserving single-frame inference efficiency. Our design is driven by a key clinical observation: the prostate exhibits geometric st
Advances in AI efficiency for medical imaging are continually being developed to meet the demands of real-world clinical applications where latency is critical.
This research addresses a practical bottleneck in real-time medical interventions by making sophisticated segmentation models usable in latency-sensitive environments.
The development allows for more accurate and timely prostate segmentation during TRUS procedures without the computational overhead of 3D networks.
- · Medical AI developers
- · Urology departments
- · Surgical robotics companies
- · Legacy 2D segmentation methods
- · Computationally intensive 3D architectures
Improved precision and safety in image-guided prostate interventions.
Accelerated adoption of AI-assisted diagnostics and therapeutics in other medical domains requiring real-time consistent segmentation.
Potential for reduced procedure times and better patient outcomes for prostate-related conditions through wider clinical integration.
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Read at arXiv cs.AI