The offical implementation of the network architecture in Paper: Scale- and Slice- aware Net for 3D segmentation of organs and musculoskeletal structures in pelvic MRI

Research Type：Machine Learning/Deep Learning，Image processing/Image analysis， Technical Research

Research Focus：Anatomy， Muscle，Musculoskeletal

S^2aNet is presented for 3D dense segmentation of 54 organs and musculoskeletal structures in female pelvic MR images. A Scale- aware module is designed to capture the spatial and semantic information of different-scale structures. A Slice-aware module is introduced to model similar spatial relationships of consecutive slices in 3D data. Moreover, S^2aNet leverages a weight-adaptive loss optimization strategy to reinforce the supervision with more discriminative capability on hard samples and categories.

• a weight-adaptive loss optimization strategy is introduced to alleviate difficult samples and the problems of class imbalance.

• a multislice-aware feature fusion module is proposed to encode and fuse features from different slices by a parameter-sharing mechanism.

• a parallel multiscale-aware module is designed to extract both spatial information of large-scale categories and semantic information of small-scale categories without losing spatial resolution.

• To our knowledge, this is the first report to achieve a 3D dense segmentation for pelvic 54 structures on MRI.

Experiments have been performed on a pelvic MRI cohort of 27 MR images from 27 patient cases. Across the cohort and 54 categories of organs and musculoskeletal structures manually delineated, S^2aNet was shown to outperform the UNet framework and other state-of-the-art fully convolutional networks in terms of sensitivity, Dice similarity coefficient and relative volume difference.

The segmentation results are given below:

You need to config the environment firstly, install python and corresponding packages, including torch, opencv, SimpleITK, and so on.

For independent evaluation, run

python A5_test.py

and if you want to generate the predicted results, run

python A6_inference25D.py 

or if you only want to infer in 2D mode:

python A6_inference2D.py 

Our network architecture files are Scale_slice_awareNet.py and net_msacunet.py, and both of them are the same.

You can also train the network on your data from scratch.

python A4_trainNet_macunet.py

!!! remember to adjust the data path or others privately to yours.

If the project helps your research, please cite the following paper:

@article{yan2022scale,
  title={Scale-and Slice-aware Net (S2aNet) for 3D segmentation of organs and musculoskeletal structures in pelvic MRI},
  author={Yan, Chaoyang and Lu, Jing-Jing and Chen, Kang and Wang, Lei and Lu, Haoda and Yu, Li and Sun, Mengyan and Xu, Jun},
  journal={Magnetic Resonance in Medicine},
  volume={87},
  number={1},
  pages={431--445},
  year={2022},
  publisher={Wiley Online Library}
}

I cherished the memories in AIMLab and thank you all for the wonderful time.

If you have any problems, just raise an issue in this repo.