Due to the intensive cost of labor and expertise in annotating 3D medical images at a voxel level, most benchmark datasets are equipped with the annotations of only one type of organs and/or tumors, resulting in the so-called partially labeling issue. To address this, we propose a dynamic on-demand network (DoDNet) that learns to segment multiple organs and tumors on partially labeled datasets. DoDNet consists of a shared encoder-decoder architecture, a task encoding module, a controller for generating dynamic convolution filters, and a single but dynamic segmentation head. The information of the current segmentation task is encoded as a task-aware prior to tell the model what the task is expected to solve. Different from existing approaches which fix kernels after training, the kernels in dynamic head are generated adaptively by the controller, conditioned on both input image and assigned task. Thus, DoDNet is able to segment multiple organs and tumors, as done by multiple networks or a multi-head network, in a much efficient and flexible manner. We have created a large-scale partially labeled dataset, termed MOTS, and demonstrated the superior performance of our DoDNet over other competitors on seven organ and tumor segmentation tasks. We also transferred the weights pre-trained on MOTS to a downstream multi-organ segmentation task and achieved state-of-the-art performance. This study provides a general 3D medical image segmentation model that has been pre-trained on a large-scale partially labelled dataset and can be extended (after fine-tuning) to downstream volumetric medical data segmentation tasks. The dataset and code areavailableat: https://git.io/DoDNet

本研究提出了一种动态需求网络（DoDNet），用于在仅标注部分器官和/或肿瘤的数据集上分割多个器官和肿瘤，通过共享编码器-解码器结构、任务编码模块、控制器和动态分割头来生成动态卷积滤波器，相对于现有方法，DoDNet更加高效，代码和数据集可在链接中获得。

DoDNet: 学习从多个部分标记的数据集中分割多个器官和肿瘤