Segmentation of side branch regions in intravascular images using multi-modal information

Fractional flow reserve (FFR) is the reference standard to identify flow-limiting coronary stenosis that requires revascularization. Accurate computation of FFR from coronary intravascular images is based on the precise reconstruction of the side branches. In this paper, a novel approach for segmentation of side branches in intravascular images is presented. The framework consists of an image-to-image translation module and two side branch region segmentation modules. By using the image-to-image translation module, information from intravascular optical coherence tomography (IVOCT) and intravascular ultrasound (IVUS) images is combined to improve the segmentation performance. The framework is trained on a total of 62475 IVOCT and 186110 IVUS images, and evaluated on an independent dataset which contains 9344 IVOCT images with 91 side branches and 39450 IVUS images with 128 side branches. The Dice coefficients of IVOCT and IVUS side branches segmentation are 0.935±0.039 and 0.856±0.038, respectively. The validation results of side branches detection are: Precision = 0.934, Recall = 0.923, F₁Score = 0.929 in IVOCT, and 0.925, 0.868, 0.895 in IVUS, accordingly. Ablation studies demonstrate excellent efficiency in incorporating multi-modal information with our proposed image-to-image translation module.