Interactive Segmentation via Deep Learning and B-Spline Explicit Active Surfaces

TL;DR

This paper introduces an interactive deep learning framework for medical image segmentation that uses B-spline explicit active surfaces, enabling real-time user edits, improved robustness, and reduced workload compared to existing clinical tools.

Contribution

It presents a novel CNN-based segmentation method incorporating B-spline explicit active surfaces for real-time, smooth, and anatomically plausible contour editing in medical images.

Findings

More robust segmentation than current CNNs

Halved perceived workload in clinical evaluation

Significantly reduced user editing time

Abstract

Automatic medical image segmentation via convolutional neural networks (CNNs) has shown promising results. However, they may not always be robust enough for clinical use. Sub-optimal segmentation would require clinician's to manually delineate the target object, causing frustration. To address this problem, a novel interactive CNN-based segmentation framework is proposed in this work. The aim is to represent the CNN segmentation contour as B-splines by utilising B-spline explicit active surfaces (BEAS). The interactive element of the framework allows the user to precisely edit the contour in real-time, and by utilising BEAS it ensures the final contour is smooth and anatomically plausible. This framework was applied to the task of 2D segmentation of the levator hiatus from 2D ultrasound (US) images, and compared to the current clinical tools used in pelvic floor disorder clinic (4DView, GE Healthcare; Zipf, Austria). Experimental results show that: 1) the proposed framework is more robust than current state-of-the-art CNNs; 2) the perceived workload calculated via the NASA-TLX index was reduced more than half for the proposed approach in comparison to current clinical tools; and 3) the proposed tool requires at least 13 seconds less user time than the clinical tools, which was significant (p=0.001).