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)
Journal:
arXiv
Published Date:
Mar 26, 2025
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.
