Pretraining with large-scale 3D volumes has a potential for improving the
segmentation performance on a target medical image dataset where the training
images and annotations are limited. Due to the high cost of acquiring
pixel-level segmentation annotations on the large-scale pretraining dataset,
pretraining with unannotated images is highly desirable. In this work, we
propose a novel self-supervised learning strategy named Volume Fusion (VF) for
pretraining 3D segmentation models. It fuses several random patches from a
foreground sub-volume to a background sub-volume based on a predefined set of
discrete fusion coefficients, and forces the model to predict the fusion
coefficient of each voxel, which is formulated as a self-supervised
segmentation task without manual annotations. Additionally, we propose a novel
network architecture based on parallel convolution and transformer blocks that
is suitable to be transferred to different downstream segmentation tasks with
various scales of organs and lesions. The proposed model was pretrained with
110k unannotated 3D CT volumes, and experiments with different downstream
segmentation targets including head and neck organs, thoracic/abdominal organs
showed that our pretrained model largely outperformed training from scratch and
several state-of-the-art self-supervised training methods and segmentation
models. The code and pretrained model are available at
this https URL

本研究提出了一种名为 Volume Fusion (VF) 的自监督学习策略，结合多个随机补丁，采用预定义的融合系数融合前景子卷和背景子卷，无需手动注释地预训练 3D CT 体数据的分割模型，结果表明该方法表现优于其他基于自监督训练和分割模型的方法。

基于大规模未标注数据预训练的基础模型用于三维医学图像分割

MIS-FM: 3D Medical Image Segmentation using Foundation Models Pretrained  on a Large-Scale Unannotated Dataset

Labeling LiDAR point clouds for training autonomous driving is extremely
expensive and difficult. LiDAR simulation aims at generating realistic LiDAR
data with labels for training and verifying self-driving algorithms more
efficiently. Recently, Neural Radiance Fields (NeRF) have been proposed for
novel view synthesis using implicit reconstruction of 3D scenes. Inspired by
this, we present NeRF-LIDAR, a novel LiDAR simulation method that leverages
real-world information to generate realistic LIDAR point clouds. Different from
existing LiDAR simulators, we use real images and point cloud data collected by
self-driving cars to learn the 3D scene representation, point cloud generation
and label rendering. We verify the effectiveness of our NeRF-LiDAR by training
different 3D segmentation models on the generated LiDAR point clouds. It
reveals that the trained models are able to achieve similar accuracy when
compared with the same model trained on the real LiDAR data. Besides, the
generated data is capable of boosting the accuracy through pre-training which
helps reduce the requirements of the real labeled data.

利用实际数据和图像，我们提出了一种新的 LiDAR 模拟方法 NeRF-LiDAR 用于生成真实的 LiDAR 点云，以便更有效地训练和验证自动驾驶算法，并可以通过预训练来提高准确性。