SAMRI-2: A Memory-based Model for Cartilage and Meniscus Segmentation in 3D MRIs of the Knee Joint
Journal:
arXiv
Published Date:
Feb 14, 2025
Abstract
Accurate morphometric assessment of cartilage-such as thickness/volume-via
MRI is essential for monitoring knee osteoarthritis. Segmenting cartilage
remains challenging and dependent on extensive expert-annotated datasets, which
are heavily subjected to inter-reader variability. Recent advancements in
Visual Foundational Models (VFM), especially memory-based approaches, offer
opportunities for improving generalizability and robustness. This study
introduces a deep learning (DL) method for cartilage and meniscus segmentation
from 3D MRIs using interactive, memory-based VFMs. To improve spatial awareness
and convergence, we incorporated a Hybrid Shuffling Strategy (HSS) during
training and applied a segmentation mask propagation technique to enhance
annotation efficiency. We trained four AI models-a CNN-based 3D-VNet, two
automatic transformer-based models (SaMRI2D and SaMRI3D), and a
transformer-based promptable memory-based VFM (SAMRI-2)-on 3D knee MRIs from
270 patients using public and internal datasets and evaluated on 57 external
cases, including multi-radiologist annotations and different data acquisitions.
Model performance was assessed against reference standards using Dice Score
(DSC) and Intersection over Union (IoU), with additional morphometric
evaluations to further quantify segmentation accuracy. SAMRI-2 model, trained
with HSS, outperformed all other models, achieving an average DSC improvement
of 5 points, with a peak improvement of 12 points for tibial cartilage. It also
demonstrated the lowest cartilage thickness errors, reducing discrepancies by
up to threefold. Notably, SAMRI-2 maintained high performance with as few as
three user clicks per volume, reducing annotation effort while ensuring
anatomical precision. This memory-based VFM with spatial awareness offers a
novel approach for reliable AI-assisted knee MRI segmentation, advancing DL in
musculoskeletal imaging.
