Deep learning platform for fully automatic segmentation and phenotyping of coronary intravascular ultrasound (IVUS) pullbacks, packaged with a desktop GUI and CLI. A convolutional encoder-decoder network delineates the internal (lumen) and external elastic lamina borders on each cross-sectional IVUS frame; downstream rule-based analysis derives lumen area, plaque area, plaque burden, automatically flags lesions with plaque burden exceeding 40%, and reports minimum lumen area and maximum plaque burden along the pullback. Also supports end-diastolic gating and manual contour editing. Trained on 305 clinical IVUS pullbacks (270 train / 35 validation) from Philips and Boston Scientific catheters at Emory University; downstream evaluations have applied DeepIVUS to tasks such as automated detection of stent underexpansion.

nnU-Net-based segmentation network that detects and delineates stenotic lesions directly from X-ray coronary angiography frames, developed for the ARCADE (MICCAI 2023) stenosis-detection challenge. A companion model (YOLO-Angio, same team) handles vessel-tree segmentation; StenUNet focuses specifically on pixel-wise localization of stenotic regions. Placed 3rd overall among ARCADE challenge entrants with an F1 score of 0.5348 on the hold-out test set, within 0.0005 of the 2nd-place team.

Segments coronary vessels from invasive X-ray angiography images and automatically quantifies the degree of stenosis along the extracted centerlines. Combines MedSAM, a Segment-Anything-style vision model, with a Mamba-based VM-UNet segmentation branch for efficient long-range feature modeling. Trained and evaluated on the ARCADE, DCA1, and GH angiography datasets by researchers at Ocean University of China and Shandong University.