Multi-atlas based neointima segmentation in intravascular coronary OCT

Neointima thickening plays a decisive role in coronary restenosis after stenting. The aim of this study is to detect neointima tissue in intravascular optical coherence tomography (IVOCT) sequences. We developed a multi-atlas based segmentation method to detect neointima without stent struts locations. The atlases are selected by measurements of stenosis and a similarity metric. The probability map is then used to estimate neointima label in the unseen image. To account for the registration errors, a patch-based label fusion approach is applied. Validation is performed using 18 typical in-vivo IVOCT sequences. The comparison against manual expert segmentation and other fusion approaches demonstrates that the proposed neointima identification is robust and accurate.