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ETD Abstract Container

Abstract Header

3D Lung Nodule Segmentation Using Difference Over Union Combo Loss for UNet

Borgman, Coleman M
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1746027719160522

Abstract Details

2025, Master of Science in Electrical Engineering, University of Dayton, Electrical and Computer Engineering.
Lung cancer is the leading cause of cancer-related deaths. Which is why early and accurate detection of pulmonary nodules in computed tomography (CT) scans is necessary. Manual segmentation of nodules by radiologists is time consuming and prone to variability, fueling the need for automated solutions. This thesis introduces a novel Difference-over Union (DoU) Combo Loss function for 3D lung nodule segmentation using a 3D U-Net architecture, trained on the LIDC-IDRI dataset. The proposed loss function combines dice loss, binary cross-entropy, and boundary DoU loss to address region, pixel, and boundary level segmentation challenges, enhancing performance on imbalanced data. Quantitative results show that the DoU Combo Loss slightly outperformed the standard Combo Loss and other baselines, like the unified focal loss. Qualitative analysis showed reduced hallucination in complex CT scans with DoU Combo Loss, improving reliability. Although the quantitative gains are modest, the proposed method demonstrates potential for robust automated nodule segmentation, offering a simpler alternative to complex architectures and paving the way for enhanced clinical diagnostics.
Russell Hardie (Advisor)
Barath Narayanan (Committee Member)
Garrett Sargent (Committee Member)
52 p.
Electrical Engineering; Medical Imaging
UNet; lung nodule; segmentation; loss function; neural network

Recommended Citations

Citations

  • Borgman, C. M. (2025). 3D Lung Nodule Segmentation Using Difference Over Union Combo Loss for UNet [Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1746027719160522

    APA Style (7th edition)

  • Borgman, Coleman. 3D Lung Nodule Segmentation Using Difference Over Union Combo Loss for UNet. 2025. University of Dayton, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1746027719160522.

    MLA Style (8th edition)

  • Borgman, Coleman. "3D Lung Nodule Segmentation Using Difference Over Union Combo Loss for UNet." Master's thesis, University of Dayton, 2025. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1746027719160522

    Chicago Manual of Style (17th edition)

dayton1746027719160522
16
© 2025, all rights reserved.
This open access ETD is published by University of Dayton and OhioLINK.
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