Publication

EPISeg: Automatic Segmentation of Spinal Cord fMRI Data

2025

Abstract

Motivation: Spinal cord fMRI holds promise for somatosensory and motor research, but challenges in data acquisition and preprocessing limit its potential. Goal(s): Develop an automated segmentation tool for spinal cord fMRI data that minimizes manual intervention and improves segmentation accuracy which is important for data analysis. Approach: We introduce EPISeg, a deep learning-based segmentation method trained on an open-source multi-site dataset of 406 subjects using active learning with human-in-the-loop feedback. Results: EPISeg outperformed established methods like PropSeg, DeepSeg, and a Contrast-agnostic model, achieving a Dice score of 0.88. Impact: EPISeg significantly enhances spinal fMRI research by enabling automated, accurate segmentations of EPI data, overcoming limitations of manual segmentation. Its integration into SCT broadens accessibility and reproducibility, facilitating robust group-level analyses essential for advancing studies of spinal processes and disorders.

Official source

https://infoscience.epfl.ch/entities/publication/197ea95f-291a-429b-844a-a33459612647

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2025

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/197ea95f-291a-429b-844a-a33459612647

About this result

Related concepts (32)

Image segmentation

In and computer vision, image segmentation is the process of partitioning a into multiple image segments, also known as image regions or image objects (sets of pixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics.

Medical image computing

Medical image computing (MIC) is an interdisciplinary field at the intersection of computer science, information engineering, electrical engineering, physics, mathematics and medicine. This field develops computational and mathematical methods for solving problems pertaining to medical images and their use for biomedical research and clinical care. The main goal of MIC is to extract clinically relevant information or knowledge from medical images.

Active learning

Active learning is "a method of learning in which students are actively or experientially involved in the learning process and where there are different levels of active learning, depending on student involvement." states that "students participate [in active learning] when they are doing something besides passively listening." According to Hanson and Moser (2003) using active teaching techniques in the classroom can create better academic outcomes for students.

Related publications (9)

EPISeg: Automatic Segmentation of Spinal Cord fMRI Data

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