ICA-UNet: ICA Inspired Statistical UNet for Real-time 3D Cardiac Cine MRI Segmentation

TL;DR

ICA-UNet is a novel neural network inspired by ICA that enables fast, accurate, real-time 3D cardiac MRI segmentation, significantly reducing latency and improving accuracy over existing methods.

Contribution

The paper introduces ICA-UNet, a new ICA-inspired neural network architecture that achieves real-time 3D cardiac MRI segmentation with high accuracy and low latency.

Findings

Outperforms state-of-the-art methods in Dice scores

Achieves up to 12.6X reduction in latency

Meets real-time throughput requirements

Abstract

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.