Integrated Deep Learning and Stochastic Models for Accurate Segmentation of Lung Nodules From Computed Tomography Images: A Novel Framework

• Published in: IEEE access Vol. 11; pp. 99807 - 99821
• Main Authors: Youssef, Bassant E., Alksas, Ahmed, Shalaby, Ahmed, Mahmoud, Ali H., Van Bogaert, Eric, Alghamdi, Norah Saleh, Neubacher, Alyssa, Contractor, Sohail, Ghazal, Mohammed, Elmaghraby, Adel S., El-Baz, Ayman
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 3D-UNet; Annotations; Computational modeling; Computed tomography; Computed tomography (CT); Datasets; Deep learning; Deformable models; Fields (mathematics); Image databases; Image segmentation; joint stochastic model; Lung; lung nodules; Lungs; Machine learning; Markov-Gibbs random field; Medical imaging; Metric space; Nodules; Random processes; segmentation; Stochastic models; Stochastic processes; Three-dimensional displays; Tomography; Tumors

This paper introduces an innovative model for precise extraction of lung nodules from 3D computed tomography (CT) scans. Our approach comprises two essential preprocessing stages aimed at refining search accuracy and nodule segmentation. Initially, we leverage a two-level joint Markov-Gibbs random f...