@INPROCEEDINGS{lance2023,
  author={Liu, Liangchen and Hou, Benjamin and Nag, Manas and Liu, Jianfei and Hasani, Navid and Shen, Thomas and Zhu, Yingying and Farhadi, Faraz and Delie, Taylor and Morris, Michael A. and Saboury, Babak and Xiao, Jing and Summers, Ronald M.},
  booktitle={2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)}, 
  title={Improving Automatic Segmentation of Lymphoma with Additional Medical Knowledge Priors}, 
  year={2023},
  volume={},
  number={},
  pages={1-4},
  doi = {10.1109/isbi53787.2023.10230634},
  url = {http://dx.doi.org/10.1109/isbi53787.2023.10230634},
  abstract = {Lymphoma is a type of malignant tumor that is often fatal to people of all ages. Positron Emission Tomography (PET) / Computed Tomography (CT) is the primary imaging method to assess lymphoma and monitor treatment response. As PET is sensitive to identify lymphoma regions while CT provides detailed anatomic information, the two imaging modalities can complement each other to enable improved diagnosis. However, automatic lymphoma segmentation is still a challenging task due to its substantial size and shape variability and limited datasets for training. To that end, we designed an automatic lymphoma segmentation model with nnU-net as the backbone. Our pipeline incorporates pre- and post- processing mechanisms to remove regions of normal increased radiotracer uptake, and augment training with non-lymphoma samples. The proposed method was validated on the PET/CT scans from forty-six patients. Experimental results revealed that the Dice coefficient improved from 0.263 to 0.477 in comparison with the baseline nnU-Net. The results and analysis demonstrate the efficacy of the proposed method.},
}