Distilling Temporal Coherence into 2D Networks for Transrectal Ultrasound Prostate Video Segmentation
arXiv cs.CV1mo4 min read
arXiv:2606.31198v1 Announce Type: new 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 stab
