ICA-UNet: ICA Inspired Statistical UNet for Real-time 3D Cardiac Cine MRI Segmentation
This addresses the problem of real-time segmentation for cardiac interventions, offering a novel method that improves both accuracy and speed over existing approaches.
The paper tackles real-time 3D cardiac cine MRI segmentation for visual assistance in interventions by proposing ICA-UNet, which achieves higher Dice scores and reduces latency by up to 12.6x compared to state-of-the-art methods.
Real-time cine magnetic resonance imaging (MRI) plays an increasingly important role in various cardiac interventions. In order to enable fast and accurate visual assistance, the temporal frames need to be segmented on-the-fly. However, state-of-the-art MRI segmentation methods are used either offline because of their high computation complexity, or in real-time but with significant accuracy loss and latency increase (causing visually noticeable lag). As such, they can hardly be adopted to assist visual guidance. In this work, inspired by a new interpretation of Independent Component Analysis (ICA) for learning, we propose a novel ICA-UNet for real-time 3D cardiac cine MRI segmentation. Experiments using the MICCAI ACDC 2017 dataset show that, compared with the state-of-the-arts, ICA-UNet not only achieves higher Dice scores, but also meets the real-time requirements for both throughput and latency (up to 12.6X reduction), enabling real-time guidance for cardiac interventions without visual lag.