Annual Conference of KIPS (한국정보처리학회:학술대회논문집)

  • 2024.10a
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  • Pages.653-655
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  • 2024
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  • 2005-0011(pISSN)
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  • 2671-7298(eISSN)
Korea Information Processing Society (한국정보처리학회)
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Precision Lung Cancer Segmentation from CT & PET Images Using Mask2Former

Mask2Former 를 이용한 CT 및 PET 영상의 정밀 폐암 분할

  • Md Ilias Bappi (Dept. of. Artificial Intelligence Convergence, Chonnam National University) ;
  • Kyungbeak Kim (Dept. of. Artificial Intelligence Convergence, Chonnam National University)
  • ;
  • 김경백 (인공지능융합학과, 전남대학교)
  • Published : 2024.10.31
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Abstract

Lung cancer is a leading cause of death worldwide, highlighting the critical need for early diagnosis. Lung image analysis and segmentation are essential steps in this process, but manual segmentation of medical images is extremely time-consuming for radiation oncologists. The complexity of this task is heightened by the significant variability in lung tumors, which can differ greatly in size, shape, and texture due to factors like tumor subtype, stage, and patient-specific characteristics. Traditional segmentation methods often struggle to accurately capture this diversity. To address these challenges, we propose a lung cancer diagnosis system based on Mask2Former, utilizing CT (Computed Tomography) and PET (Positron Emission Tomography) images. This system excels in generating high-quality instance segmentation masks, enabling it to better adapt to the heterogeneous nature of lung tumors compared to traditional methods. Additionally, our system classifies the segmented output as either benign or malignant, leveraging a self-supervised network. The proposed approach offers a powerful tool for early diagnosis and effective management of lung cancer using CT and PET data. Extensive experiments demonstrate its effectiveness in achieving improved segmentation and classification results.

Keywords

Acknowledgement

This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) under the Artificial Intelligence Convergence Innovation Human Resources Development (IITP-2023-RS-2023-00256629) grant funded by the Korea government(MSIT). This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2024-RS-2024-00437718) supervised by the IITP(Institute for Information & Communications Technology Planning & Evaluation).

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