2d U-net models trained to segment human placental maternal/fetal blood volumes and blood vessels from syncrotron micro-CT data along with a sample data volume.

This dataset contains a 512 x 512 x 512 pixel volume taken from an imaging dataset of human placental tissue collected at Diamond Light Source Manchester Imaging Branchline, I13-2 on visits MG23941 and MG22562 using in-line high-resolution synchrotron-sourced phase contrast micro-computed X-ray tomography. This data is saved in HDF5 format with a uint8 datatype. Alongside this are two 2d binary U-net models that have been trained to segment this data. One model segments the data into regions of maternal/fetal blood volume, the other segments the blood vessels. Both models were trained using the fastai python package, which utilises the pytorch library. These models were used to segment the data in our paper "A massively multi-scale approach to characterising tissue architecture by synchrotron micro-CT applied to the human placenta" which can be found at https://www.biorxiv.org/content/10.1101/2020.12.07.411462v1. The code used for training the U-net models and for predicting the segmentation of the data volume can be found at https://github.com/DiamondLightSource/placental-segmentation-2dunet and is published at https://doi.org/10.5281/zenodo.4252562本数据集包含一组512×512×512像素的三维体数据，该数据采自钻石光源（Diamond Light Source）曼彻斯特成像支线I13-2的人类胎盘组织成像数据集，采集批次为MG23941与MG22562，采用在线高分辨率同步辐射相衬显微X射线计算机断层扫描技术获取。该数据以HDF5格式存储，数据类型为uint8。本数据集附带两个经训练的二维二进制U型网络（U-net）模型，用于该数据集的分割任务：其一用于分割母胎血容积区域，其二用于分割血管。两个模型均基于fastai Python库开发，该库依赖PyTorch库。上述模型已应用于本团队发表的论文《基于同步辐射显微CT的多尺度人类胎盘组织架构表征方法》（A massively multi-scale approach to characterising tissue architecture by synchrotron micro-CT applied to the human placenta），论文链接为https://www.biorxiv.org/content/10.1101/2020.12.07.411462v1。用于训练U型网络模型及预测该体数据分割结果的代码可通过https://github.com/DiamondLightSource/placental-segmentation-2dunet 获取，该代码已发布于https://doi.org/10.5281/zenodo.4252562