Segmentations are crucial in medical imaging to obtain morphological, volumetric, and radiomics biomarkers. Manual segmentation is accurate but not feasible in the radiologist's clinical workflow, while automatic segmentation generally obtains sub-par performance. We therefore developed a minimally interactive deep learning-based segmentation method for soft-tissue tumors (STTs) on CT and MRI. The method requires the user to click six points near the tumor's extreme boundaries. These six points are transformed into a distance map and serve, with the image, as input for a Convolutional Neural Network. For training and validation, a multicenter dataset containing 514 patients and nine STT types in seven anatomical locations was used, resulting in a Dice Similarity Coefficient (DSC) of 0.85$\pm$0.11 (mean $\pm$ standard deviation (SD)) for CT and 0.84$\pm$0.12 for T1-weighted MRI, when compared to manual segmentations made by expert radiologists. Next, the method was externally validated on a dataset including five unseen STT phenotypes in extremities, achieving 0.81$\pm$0.08 for CT, 0.84$\pm$0.09 for T1-weighted MRI, and 0.88\pm0.08 for previously unseen T2-weighted fat-saturated (FS) MRI. In conclusion, our minimally interactive segmentation method effectively segments different types of STTs on CT and MRI, with robust generalization to previously unseen phenotypes and imaging modalities.

翻译：分割在医学影像中至关重要，用于获取形态学、体积和影像组学生物标志物。手动分割虽然精确，但在放射科医生的临床工作流程中难以实施，而自动分割通常性能欠佳。因此，我们开发了一种基于深度学习的CT和MRI软组织肿瘤（STTs）最小交互式分割方法。该方法要求用户在肿瘤极端边界附近点击六个点。这六个点被转换为距离图，并与图像一起作为卷积神经网络的输入。在训练和验证阶段，使用了包含514名患者、涉及七个解剖位置、九种STT类型的多中心数据集。与专家放射科医师的手动分割相比，该方法在CT上获得的Dice相似系数（DSC）为0.85±0.11（均值±标准差（SD）），在T1加权MRI上为0.84±0.12。随后，该方法在包含五种未见过的肢体STT表型的数据集上进行了外部验证，在CT上达到0.81±0.08，在T1加权MRI上达到0.84±0.09，在之前未见的T2加权脂肪饱和（FS）MRI上达到0.88±0.08。总之，我们的最小交互式分割方法能有效分割CT和MRI上不同类型的STT，并对之前未见过的表型和成像模态具有稳健的泛化能力。