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Magnetic Resonance Imaging

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Updated: May 31, 2025

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

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Published on: January 7, 2019

Advanced Automated Model for Robust Bone Marrow Segmentation in Whole-body MRI.

Fabian Bauer¹, Jessica Kächele², Juliane Bernhard³

¹Division of Radiology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany (F.B., M.G., H.P.S., S.D.); Division of Musculoskeletal Imaging and Intervention, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114 (F.B.).

Academic Radiology

|January 23, 2025

Summary

This summary is machine-generated.

An advanced automated bone marrow segmentation model for whole-body MRI in monoclonal plasma cell disorders (MPCD) shows reliable performance. This AI model accurately segments bone marrow spaces, even with severe pathologies across multiple centers.

Keywords:

Deep Learning Magnetic Resonance Imaging Multiple Myeloma Segmentation

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Area of Science:

Radiology and Medical Imaging
Artificial Intelligence in Medicine
Oncology

Background:

Monoclonal plasma cell disorders (MPCD) require accurate assessment of bone marrow involvement.
Whole-body MRI (WB-MRI) is a key imaging modality for MPCD.
Automated segmentation of bone marrow spaces (BMS) can improve efficiency and consistency.

Purpose of the Study:

To develop an advanced automated BMS segmentation model using nnU-Net for WB-MRI in MPCD.
To validate the model's performance on multicenter datasets with diverse pathologies.
To compare the advanced model against a previously established basic segmentation model.

Main Methods:

Utilized a cohort of 210 WB-MRIs from 207 MPCD patients across 8 centers.
Trained an nnU-Net algorithm on 186 T1-weighted WB-MRIs.
Tested the model on independent datasets, including cases with extensive tumor load and varying image quality.

Main Results:

Achieved high mean Dice scores for BMS segmentation: 0.89±0.13 (test set I) and 0.88±0.11 (test set II).
Demonstrated significantly improved performance compared to a prior basic model (p<0.05).
Accurately segmented bone marrow affected by pathologies, artifacts, and low imaging quality.

Conclusions:

The advanced automated model provides reliable segmentation of BMS on multicenter WB-MRI data.
The model is robust even in the presence of severe myeloma-related pathologies and imaging heterogeneity.
This technology holds promise for improved quantitative analysis in MPCD management.