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.

翻译：现有关于慢性下背痛的研究通常聚焦于单一或少数特定组织，而非进行全面的逐层分析。由于三维图像常包含数百个切片，对这些解剖结构进行人工标注既耗时又易出错。本研究旨在开发并验证一种名为InterSliceBoost的新方法，使其能够在部分标注的数据集上训练分割模型，且不损害分割性能。InterSliceBoost的架构包含两个组件：层间生成器与分割模型。生成器采用基于残差块的编码器从相邻图像-掩码对中提取特征，计算差分特征后输入解码器以生成层间图像-掩码对。分割模型在部分标注数据集（如间隔1、2、3或7张图像）及生成的层间图像-掩码对上进行训练。为验证InterSliceBoost的性能，我们使用了一项正在进行中的慢性下背痛研究采集的29名受试者共76组B型超声扫描数据集。仅使用33%图像切片训练的InterSliceBoost在独立测试集上对全部六层组织取得了80.84%的平均Dice系数，其中真皮、浅层脂肪、浅筋膜、深层脂肪、深筋膜及肌肉的分割Dice系数分别为73.48%、61.11%、81.87%、95.74%、83.52%与88.74%。该性能显著高于基于全标注图像训练的传统模型（p<0.05）。InterSliceBoost能够在部分标注条件下有效分割三维B型超声图像中描绘的六层组织。