Automated Identification and Segmentation of Hi Sources in CRAFTS Using
  Deep Learning Method
Zihao Song Huaxi Chen Donghui Quan Di Li Yinghui Zheng Shulei Ni Yunchuan Chen Yun Zheng
We introduce a machine learning-based method for extracting HI sources from
3D spectral data, and construct a dedicated dataset of HI sources from CRAFTS.
Our custom dataset provides comprehensive resources for HI source detection.
Utilizing the 3D-Unet segmentation architecture, our method reliably identifies
and segments HI sources, achieving notable performance metrics with recall
rates reaching 91.6% and accuracy levels at 95.7%. These outcomes substantiate
the value of our custom dataset and the efficacy of our proposed network in
identifying HI source. Our code is publicly available at
https://github.com/fishszh/HISF.