A weakly-supervised deep learning model for fast localisation and delineation of the skeleton, internal organs, and spinal canal on Whole-Body Diffusion-Weighted MRI (WB-DWI)

• URL: http://arxiv.org/abs/2503.20722v1
• Date: Wed, 26 Mar 2025 17:03:46 GMT
• Title: A weakly-supervised deep learning model for fast localisation and delineation of the skeleton, internal organs, and spinal canal on Whole-Body Diffusion-Weighted MRI (WB-DWI)
• Authors: A. Candito, A. Dragan, R. Holbrey, A. Ribeiro, R. Donners, C. Messiou, N. Tunariu, D. -M. Koh, M. D. Blackledge, The Institute of Cancer Research, London, United Kingdom, The Royal Marsden NHS Foundation Trust, London, United Kingdom, University Hospital Basel, Basel, Switzerland,
• Abstract summary: Apparent Diffusion Coefficient (ADC) values and Total Diffusion Volume (TDV) from Whole-body diffusion-weighted MRI (WB-DWI) are recognized cancer imaging biomarkers.<n>As a first step, we propose an algorithm to generate fast and reproducible probability maps of the skeleton, adjacent internal organs (liver, spleen, urinary bladder, and kidneys), and spinal canal.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Background: Apparent Diffusion Coefficient (ADC) values and Total Diffusion Volume (TDV) from Whole-body diffusion-weighted MRI (WB-DWI) are recognized cancer imaging biomarkers. However, manual disease delineation for ADC and TDV measurements is unfeasible in clinical practice, demanding automation. As a first step, we propose an algorithm to generate fast and reproducible probability maps of the skeleton, adjacent internal organs (liver, spleen, urinary bladder, and kidneys), and spinal canal. Methods: We developed an automated deep-learning pipeline based on a 3D patch-based Residual U-Net architecture that localizes and delineates these anatomical structures on WB-DWI. The algorithm was trained using "soft-labels" (non-binary segmentations) derived from a computationally intensive atlas-based approach. For training and validation, we employed a multi-center WB-DWI dataset comprising 532 scans from patients with Advanced Prostate Cancer (APC) or Multiple Myeloma (MM), with testing on 45 patients. Results: Our weakly-supervised deep learning model achieved an average dice score/precision/recall of 0.66/0.6/0.73 for skeletal delineations, 0.8/0.79/0.81 for internal organs, and 0.85/0.79/0.94 for spinal canal, with surface distances consistently below 3 mm. Relative median ADC and log-transformed volume differences between automated and manual expert-defined full-body delineations were below 10% and 4%, respectively. The computational time for generating probability maps was 12x faster than the atlas-based registration algorithm (25 s vs. 5 min). An experienced radiologist rated the model's accuracy "good" or "excellent" on test datasets. Conclusion: Our model offers fast and reproducible probability maps for localizing and delineating body regions on WB-DWI, enabling ADC and TDV quantification, potentially supporting clinicians in disease staging and treatment response assessment.

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