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。

DoDNet: 学习从多个部分标记的数据集中分割多个器官和肿瘤

DoDNet: Learning to segment multi-organ and tumors from multiple  partially labeled datasets

Current multi-object tracking and segmentation (MOTS) methods follow the
tracking-by-detection paradigm and adopt convolutions for feature extraction.
However, as affected by the inherent receptive field, convolution based feature
extraction inevitably mixes up the foreground features and the background
features, resulting in ambiguities in the subsequent instance association. In
this paper, we propose a highly effective method for learning instance
embeddings based on segments by converting the compact image representation to
un-ordered 2D point cloud representation. Our method generates a new
tracking-by-points paradigm where discriminative instance embeddings are
learned from randomly selected points rather than images. Furthermore, multiple
informative data modalities are converted into point-wise representations to
enrich point-wise features. The resulting online MOTS framework, named
PointTrack, surpasses all the state-of-the-art methods including 3D tracking
methods by large margins (5.4% higher MOTSA and 18 times faster over
MOTSFusion) with the near real-time speed (22 FPS). Evaluations across three
datasets demonstrate both the effectiveness and efficiency of our method.
Moreover, based on the observation that current MOTS datasets lack crowded
scenes, we build a more challenging MOTS dataset named APOLLO MOTS with higher
instance density. Both APOLLO MOTS and our codes are publicly available at
this https URL

本文提出了一种新的多对象跟踪方法，该方法利用无序的 2D 点云表示来生成实例嵌入，从随机选择的点中学习辨别实例嵌入，利用多种信息数据模态来丰富点特征，基于此建立了在线 MOTS 系统 PointTrack，在三个数据集上进行评估，并构建了一个更具挑战性的 MOTS 数据集 APOLLO MOTS。