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: this https URL

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。