A Soft STAPLE Algorithm Combined with Anatomical Knowledge

TL;DR

This paper introduces a soft STAPLE algorithm that fuses probabilistic expert annotations in medical imaging, leveraging anatomical knowledge to improve segmentation accuracy for MS lesions.

Contribution

It proposes a novel soft STAPLE algorithm that handles uncertain expert markings and incorporates anatomical information, enhancing consensus segmentation in medical imaging.

Findings

Improved Dice similarity coefficient on MS segmentation

Enhanced precision-recall tradeoff in results

Effective fusion of soft expert annotations

Abstract

Supervised machine learning algorithms, especially in the medical domain, are affected by considerable ambiguity in expert markings. In this study we address the case where the experts' opinion is obtained as a distribution over the possible values. We propose a soft version of the STAPLE algorithm for experts' markings fusion that can handle soft values. The algorithm was applied to obtain consensus from soft Multiple Sclerosis (MS) segmentation masks. Soft MS segmentations are constructed from manual binary delineations by including lesion surrounding voxels in the segmentation mask with a reduced confidence weight. We suggest that these voxels contain additional anatomical information about the lesion structure. The fused masks are utilized as ground truth mask to train a Fully Convolutional Neural Network (FCNN). The proposed method was evaluated on the MICCAI 2016 challenge dataset, and yields improved precision-recall tradeoff and a higher average Dice similarity coefficient.