FastVentricle: Cardiac Segmentation with ENet

TL;DR

FastVentricle introduces a highly efficient FCN architecture based on ENet for rapid and memory-efficient cardiac ventricular segmentation in CMR images, maintaining high accuracy while significantly reducing computational resources.

Contribution

The paper presents FastVentricle, a novel FCN architecture based on ENet, optimized for fast and memory-efficient cardiac segmentation in CMR imaging.

Findings

4x faster segmentation speed

6x less memory usage

Maintains high clinical accuracy

Abstract

Cardiac Magnetic Resonance (CMR) imaging is commonly used to assess cardiac structure and function. One disadvantage of CMR is that post-processing of exams is tedious. Without automation, precise assessment of cardiac function via CMR typically requires an annotator to spend tens of minutes per case manually contouring ventricular structures. Automatic contouring can lower the required time per patient by generating contour suggestions that can be lightly modified by the annotator. Fully convolutional networks (FCNs), a variant of convolutional neural networks, have been used to rapidly advance the state-of-the-art in automated segmentation, which makes FCNs a natural choice for ventricular segmentation. However, FCNs are limited by their computational cost, which increases the monetary cost and degrades the user experience of production systems. To combat this shortcoming, we have developed the FastVentricle architecture, an FCN architecture for ventricular segmentation based on the recently developed ENet architecture. FastVentricle is 4x faster and runs with 6x less memory than the previous state-of-the-art ventricular segmentation architecture while still maintaining excellent clinical accuracy.