Interactive segmentation of medical images through fully convolutional neural networks

TL;DR

This paper introduces a semi-automated, deep learning-based interactive segmentation method for medical images that requires minimal user input, generalizes well, and allows quick, precise corrections, improving clinical workflow.

Contribution

The paper presents a novel deep learning approach for interactive medical image segmentation that balances automation with user control, requiring few clicks and enabling fast, accurate corrections.

Findings

Requires only 1-3 user clicks for high-quality segmentation

Generalizes well to unseen structures and cases

Allows quick, intuitive corrections for arbitrary precision

Abstract

Image segmentation plays an essential role in medicine for both diagnostic and interventional tasks. Segmentation approaches are either manual, semi-automated or fully-automated. Manual segmentation offers full control over the quality of the results, but is tedious, time consuming and prone to operator bias. Fully automated methods require no human effort, but often deliver sub-optimal results without providing users with the means to make corrections. Semi-automated approaches keep users in control of the results by providing means for interaction, but the main challenge is to offer a good trade-off between precision and required interaction. In this paper we present a deep learning (DL) based semi-automated segmentation approach that aims to be a "smart" interactive tool for region of interest delineation in medical images. We demonstrate its use for segmenting multiple organs on computed tomography (CT) of the abdomen. Our approach solves some of the most pressing clinical challenges: (i) it requires only one to a few user clicks to deliver excellent 2D segmentations in a fast and reliable fashion; (ii) it can generalize to previously unseen structures and "corner cases"; (iii) it delivers results that can be corrected quickly in a smart and intuitive way up to an arbitrary degree of precision chosen by the user and (iv) ensures high accuracy. We present our approach and compare it to other techniques and previous work to show the advantages brought by our method.