  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.
