Spinal metastasis is the most common disease in bone metastasis and may cause pain, instability and neurological injuries. Early detection of spinal metastasis is critical for accurate staging and optimal treatment. The diagnosis is usually facilitated with Computed Tomography (CT) scans, which requires considerable efforts from well-trained radiologists. In this paper, we explore a learning-based automatic bone quality classification method for spinal metastasis based on CT images. We simultaneously take the posterolateral spine involvement classification task into account, and employ multi-task learning (MTL) technique to improve the performance. MTL acts as a form of inductive bias which helps the model generalize better on each task by sharing representations between related tasks. Based on the prior knowledge that the mixed type can be viewed as both blastic and lytic, we model the task of bone quality classification as two binary classification sub-tasks, i.e., whether blastic and whether lytic, and leverage a multiple layer perceptron to combine their predictions. In order to make the model more robust and generalize better, self-paced learning is adopted to gradually involve from easy to more complex samples into the training process. The proposed learning-based method is evaluated on a proprietary spinal metastasis CT dataset. At slice level, our method significantly outperforms an 121-layer DenseNet classifier in sensitivities by $+12.54\%$, $+7.23\%$ and $+29.06\%$ for blastic, mixed and lytic lesions, respectively, meanwhile $+12.33\%$, $+23.21\%$ and $+34.25\%$ at vertebrae level.

翻译：脊柱转移是骨转移中最常见的疾病，可能导致疼痛、不稳定及神经损伤。早期检测脊柱转移对于准确分期和优化治疗至关重要。诊断通常借助计算机断层扫描（CT）实现，这需要训练有素的放射科医生付出大量努力。本文探索了一种基于CT图像的、基于学习的脊柱转移瘤骨质量自动分类方法。我们同时考虑后外侧脊柱受累分类任务，并采用多任务学习（MTL）技术来提升性能。MTL作为一种归纳偏置形式，通过共享相关任务间的表征，帮助模型在每个任务上更好地泛化。基于混合型可同时视为成骨性和溶骨性的先验知识，我们将骨质量分类任务建模为两个二分类子任务（即是否成骨性和是否溶骨性），并利用多层感知器组合其预测结果。为提升模型鲁棒性和泛化能力，采用自步学习逐步将样本从简单到复杂纳入训练过程。所提出的基于学习方法在专有的脊柱转移CT数据集上进行了评估。在切片层面，我们的方法在成骨性、混合性和溶骨性病变的灵敏度上分别显著优于121层DenseNet分类器 +12.54%、+7.23%和+29.06%，在椎体层面则分别提升+12.33%、+23.21%和+34.25%。