Lung Nodule Segmentation for Explainable AI-based Cancer Screening
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Author/Creator ORCID
Date
2021-01-01
Type of Work
Department
Computer Science and Electrical Engineering
Program
Computer Science
Citation of Original Publication
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Distribution Rights granted to UMBC by the author.
Access limited to the UMBC community. Item may possibly be obtained via Interlibrary Loan thorugh a local library, pending author/copyright holder's permission.
Distribution Rights granted to UMBC by the author.
Access limited to the UMBC community. Item may possibly be obtained via Interlibrary Loan thorugh a local library, pending author/copyright holder's permission.
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Abstract
We present a novel approach for segmentation and identification of lung nodules in CT scans, for the purpose of Explainable AI assisted screening. Our segmentation approach combines the U-Net segmentation architecture with a graph-based connected component analysis for false positive nodule identification. CADe systems with high true nodule detection rate and low false positive nodules are desired. We also develop a 3D nodule dataset that can be used to build an explainable classification model for nodule malignancy and biomarkers estimation. We train and evaluate the segmentation model based on its percentage of true nodules identified within the LIDC dataset which contains 1018 CT scans and nodule annotations marked by four board-certified radiologists. We further present results of the segmentation and nodule filtering algorithm and a description of 3D nodule dataset generated.