Thesis
integrityNet: a deep learning approach for pulmonary fissure integrity classification
University of Iowa
Master of Science (MS), University of Iowa
Summer 2021
DOI: 10.17077/etd.006044
Abstract
Lungs are regularly imaged through Computed Tomography (CT). The submillimeter resolution allows for intricate structures and features within the lungs to be visualized and analyzed. Quantitative data and region segmentation from CT images is essential to efficiently process data in clinical and research settings. Deep neural networks have been shown to be able to effectively automate difficult and complex diagnoses and segmentations from medical images.
Within the lungs there are separate regions, called lobes, separated by surfaces, called fissures. Fissures can completely or partially separate the lobes. Regions of incompleteness are known to be a biomarker of collateral ventilation between lobes which is clinically relevant for endobronchial valve treatment patient screening. Beyond this, the extent and location of fissure completeness may impact lung biomechanics and have implications in disease susceptibility and progression. Automated quantification and localization of fissure completeness, or integrity, is needed to study these areas of research in large datasets.
In this work, a deep learning technique is utilized to automate fissure integrity segmentation. Using a state-of-the-art fissure segmention technique to provide input localization information, the network was able to leverage the predictive power of the fissure segmentation method to improve the model’s performance. Furthermore, the U-Net structure of the proposed network allows for better spatial prediction through the use of high-level features during image reconstruction.The model was trained and evaluated using a radiologist trained hand segmented imaging dataset. Two tasks were assessed: 1) fissure integrity classification into different categories defined by ranges of fissure completeness percentages and 2) fissure integrity localization to produce images labeling regions of incompleteness along the fissure surface.
The results demonstrate that the network was able to effectively quantify fissure integrity percentage and produce fissure integrity images similar to the ground truth images in the dataset. The model achieved high accuracy in both tasks and has potential to be used in widescale investigation of fissure integrity trends and relations to biomechanics and disease.
Details
- Title: Subtitle
- integrityNet: a deep learning approach for pulmonary fissure integrity classification
- Creators
- Zachary W. Althof
- Contributors
- Joseph M Reinhardt (Advisor)Gary E Christensen (Committee Member)Eric A Hoffman (Committee Member)Sajan G Lingala (Committee Member)Osama I Saba (Committee Member)
- Resource Type
- Thesis
- Degree Awarded
- Master of Science (MS), University of Iowa
- Degree in
- Biomedical Engineering
- Date degree season
- Summer 2021
- DOI
- 10.17077/etd.006044
- Publisher
- University of Iowa
- Number of pages
- xiii, 59 pages
- Copyright
- Copyright 2021 Zachary W. Althof
- Grant note
- This work was supported by NIH grant HL142625. SPIROMICS was supported by contracts from the NIH/NHLBI (HHSN268200900013C, HHSN268200900014C, HHSN268200900015C, HHSN268200900016C, HHSN268200900017C, HHSN268200900018C, HHSN268200900019C, HHSN268200900020C), grants from the NIH/NHLBI (U01 HL137880 and U24 HL141762), and supplemented by contributions made through the Foundation for the NIH and the COPD Foundation from AstraZeneca/MedImmune; Bayer; Bellerophon Therapeutics; Boehringer-Ingelheim Pharmaceuticals, Inc.; Chiesi Farmaceutici S.p.A.; Forest Research Institute, Inc.; GlaxoSmithKline; Grifols Therapeutics, Inc.; Ikaria, Inc.; Novartis Pharmaceuticals Corporation; Nycomed GmbH; ProterixBio; Regeneron Pharmaceuticals, Inc.; Sanofi; Sunovion; Takeda Pharmaceutical Company; and Theravance Biopharma and Mylan.
- Language
- English
- Description illustrations
- color illustrations
- Description bibliographic
- Includes bibliographical references (pages 56-59).
- Public Abstract (ETD)
Computer cognition through neural networks modeled after biological systems have shown great promise in learning complex patterns in image data. Medical images are used to noninvasively view structures within the body for diagnosis and localization of disease. Use of computer cognition to extract meaningful data from medical images has been shown to automate diagnostic and segmentation tasks that improve their precision and speed.
In this work, a computer cognition approach for a localization task within the lungs was proposed. The lungs are made up of separate regions separated by a boundary. This boundary, called a fissure, can completely or partially separate the regions within the lungs. The proposed method was shown to be able to identify regions where the boundary was incomplete from medical images of the lungs. This will aid in research of the fissure’s relation to how the lungs expand and contract during breathing and to disease progression and susceptibility. Overall, the proposed method achieved high accuracy in identifying these regions of incompleteness.
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering
- Record Identifier
- 9984124571102771
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