InterSliceBoost: Identifying Tissue Layers in Three-dimensional Ultrasound Images for Chronic Lower Back Pain (cLBP) Assessment
Journal:
arXiv
Published Date:
Mar 25, 2025
Abstract
Available studies on chronic lower back pain (cLBP) typically focus on one or
a few specific tissues rather than conducting a comprehensive layer-by-layer
analysis. Since three-dimensional (3-D) images often contain hundreds of
slices, manual annotation of these anatomical structures is both time-consuming
and error-prone. We aim to develop and validate a novel approach called
InterSliceBoost to enable the training of a segmentation model on a partially
annotated dataset without compromising segmentation performance. The
architecture of InterSliceBoost includes two components: an inter-slice
generator and a segmentation model. The generator utilizes residual block-based
encoders to extract features from adjacent image-mask pairs (IMPs).
Differential features are calculated and input into a decoder to generate
inter-slice IMPs. The segmentation model is trained on partially annotated
datasets (e.g., skipping 1, 2, 3, or 7 images) and the generated inter-slice
IMPs. To validate the performance of InterSliceBoost, we utilized a dataset of
76 B-mode ultrasound scans acquired on 29 subjects enrolled in an ongoing cLBP
study. InterSliceBoost, trained on only 33% of the image slices, achieved a
mean Dice coefficient of 80.84% across all six layers on the independent test
set, with Dice coefficients of 73.48%, 61.11%, 81.87%, 95.74%, 83.52% and
88.74% for segmenting dermis, superficial fat, superficial fascial membrane,
deep fat, deep fascial membrane, and muscle. This performance is significantly
higher than the conventional model trained on fully annotated images (p<0.05).
InterSliceBoost can effectively segment the six tissue layers depicted on 3-D
B-model ultrasound images in settings with partial annotations.
