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Improving Automatic Segmentation of lymphoma with Additional Medical Knowledge Priors

文献类型： 会议论文

第一作者： Liangchen Liu

作者： Liangchen Liu 1 ; Benjamin Hou 1 ; Manas Kumar Nag 1 ; Jianfei Liu 1 ; Navid Hasani 1 ; Thomas C. Shen 1 ; Yingying Zhu 2 ; Babak Saboury 1 ; Jing Xiao 3 ; Ronald M. Summers 1 ;

作者机构： 1.Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA

2.Department of Computer Science and Engineering, The University of Texas at Arlinton, Arlington, TX, USA

3.Ping An Technology, Shenzhen, China

关键词： Training;Image segmentation;Shape;Computed tomography;Pipelines;Positron emission tomography;Radiotracer

会议名称： IEEE International Symposium on Biomedical Imaging

主办单位：

页码： 1-5

摘要： 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.

分类号： tp391.4

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