Automated segmentation of ultrasound images can assist medical experts with diagnostic and therapeutic procedures. Although using the common modality of ultrasound, one typically needs separate datasets in order to segment, for example, different anatomical structures or lesions with different levels of malignancy. In this paper, we consider the problem of jointly learning from heterogeneous datasets so that the model can improve generalization abilities by leveraging the inherent variability among datasets. We merge the heterogeneous datasets into one dataset and refer to each component dataset as a subgroup. We propose to train a single segmentation model so that the model can adapt to each sub-group. For robust segmentation, we leverage recently proposed Segment Anything model (SAM) in order to incorporate sub-group information into the model. We propose SAM with Condition Embedding block (CEmb-SAM) which encodes sub-group conditions and combines them with image embeddings from SAM. The conditional embedding block effectively adapts SAM to each image sub-group by incorporating dataset properties through learnable parameters for normalization. Experiments show that CEmb-SAM outperforms the baseline methods on ultrasound image segmentation for peripheral nerves and breast cancer. The experiments highlight the effectiveness of Cemb-SAM in learning from heterogeneous datasets in medical image segmentation tasks.

翻译：超声图像的自动分割可辅助医疗专家进行诊断和治疗操作。尽管使用相同的超声模态，通常需要针对不同解剖结构或不同恶性程度的病变分别准备数据集。本文研究了异构数据集的联合学习问题，旨在通过利用数据集间的固有变异性提升模型的泛化能力。我们将异构数据集合并为单一数据集，并将每个组成数据集称为子群。我们提出训练单一分割模型，使其能够适应每个子群。为增强分割鲁棒性，我们利用近期提出的SAM模型（Segment Anything Model）将子群信息融入模型。我们提出带条件嵌入块的SAM模型（CEmb-SAM），该模型编码子群条件并将其与SAM的图像嵌入相结合。条件嵌入块通过可学习的归一化参数融入数据集特性，从而有效调整SAM对每个图像子群的适应性。实验表明，CEmb-SAM在周围神经和乳腺癌超声图像分割任务中优于基线方法。这些结果凸显了CEmb-SAM在医学图像分割任务中从异构数据集进行联合学习的有效性。