{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/110630"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/110630","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Deep learning methods for real-time corneal and needle segmentation in volumetric OCT scans","abstract":"Deep lamellar anterior keratoplasty (DALK) is a promising cornea transplant procedure, which mitigates the risks associated with the commonly used alternative penetrating keratoplasty. In DALK, a surgeon must insert a needle into the cornea to a precise depth and inject an air bubble to separate the stroma and endothelial layer. Optical coherence tomography (OCT) is used to help surgeons judge the needle insertion depth. However, the needle obscures the part of the cornea underneath it, making it difficult to estimate needle insertion depth. In this thesis, deep learning methods are explored for cornea and needle segmentation to automatically compute needle insertion depth from volumetric OCT scans. A simple post-processing step is applied to the mask produced by the deep neural networks to determine the coordinates of the top and bottom corneal boundaries and the needle tip. We compare the performance of 2D and 3D networks on this task. Our experiments consistently showed that all networks perform well on cornea segmentation in scans without a needle, and 3D networks perform better in scans with a needle. We also show that these deep learning methods perform better than existing graph-theory methods on this task and can segment in real-time when deployed using the NVIDIA TensorRT framework.","abstract_html":"Deep lamellar anterior keratoplasty (DALK) is a promising cornea transplant procedure, which mitigates the risks associated with the commonly used alternative penetrating keratoplasty. In DALK, a surgeon must insert a needle into the cornea to a precise depth and inject an air bubble to separate the stroma and endothelial layer. Optical coherence tomography (OCT) is used to help surgeons judge the needle insertion depth. However, the needle obscures the part of the cornea underneath it, making it difficult to estimate needle insertion depth. In this thesis, deep learning methods are explored for cornea and needle segmentation to automatically compute needle insertion depth from volumetric OCT scans. A simple post-processing step is applied to the mask produced by the deep neural networks to determine the coordinates of the top and bottom corneal boundaries and the needle tip. We compare the performance of 2D and 3D networks on this task. Our experiments consistently showed that all networks perform well on cornea segmentation in scans without a needle, and 3D networks perform better in scans with a needle. We also show that these deep learning methods perform better than existing graph-theory methods on this task and can segment in real-time when deployed using the NVIDIA TensorRT framework.","abstract_has_math":false,"creators":["Agrawal, Harsh"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Hauser, Kris"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-09-17T02:34:13Z","date_published":"2021-09-17T02:34:13Z","updated_at":"2026-07-22T22:24:52Z","subjects":["deep learning","machine learning","image segmentation","OCT","cornea segmentation","tool tracking"],"languages":["en"],"rights":["Copyright 2021 Harsh Agrawal"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/110630","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Hauser, Kris"]},{"key":"dc:creator","label":"Author","values":["Agrawal, Harsh"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2021-09-17T02:34:13Z","2023-09-17T02:34:57Z","2021-04-26","2021-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["deep learning","machine learning","image segmentation","OCT","cornea segmentation","tool tracking"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2021 Harsh Agrawal"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/110630"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Deep lamellar anterior keratoplasty (DALK) is a promising cornea transplant procedure, which mitigates the risks associated with the commonly used alternative penetrating keratoplasty. In DALK, a surgeon must insert a needle into the cornea to a precise depth and inject an air bubble to separate the stroma and endothelial layer. Optical coherence tomography (OCT) is used to help surgeons judge the needle insertion depth. However, the needle obscures the part of the cornea underneath it, making it difficult to estimate needle insertion depth. In this thesis, deep learning methods are explored for cornea and needle segmentation to automatically compute needle insertion depth from volumetric OCT scans. A simple post-processing step is applied to the mask produced by the deep neural networks to determine the coordinates of the top and bottom corneal boundaries and the needle tip. We compare the performance of 2D and 3D networks on this task. Our experiments consistently showed that all networks perform well on cornea segmentation in scans without a needle, and 3D networks perform better in scans with a needle. We also show that these deep learning methods perform better than existing graph-theory methods on this task and can segment in real-time when deployed using the NVIDIA TensorRT framework.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-05-01","The student, Harsh Agrawal, accepted the attached license on 2021-04-22 at 16:20.","The student, Harsh Agrawal, submitted this Thesis for approval on 2021-04-22 at 16:48.","This Thesis was approved for publication on 2021-04-26 at 11:33.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16168 on 2021-09-16 at 17:01:48","Made available in DSpace on 2021-09-17T02:34:13Z (GMT). No. of bitstreams: 2 AGRAWAL-THESIS-2021.pdf: 4131247 bytes, checksum: 5f69209391e2c155071baa2741ea6b5d (MD5) LICENSE.txt: 4210 bytes, checksum: 18eeeaf2dba94af3a7c57f8ed31e6122 (MD5) Previous issue date: 2021-04-26","Embargo set by: Seth Robbins for item 118473 Lift date: 2023-09-17T02:34:57Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Deep learning methods for real-time corneal and needle segmentation in volumetric OCT scans"]}]}],"canonical_facts":{"dc:contributor":["Hauser, Kris"],"dc:creator":["Agrawal, Harsh"],"dc:date":["2021-09-17T02:34:13Z","2023-09-17T02:34:57Z","2021-04-26","2021-05"],"dc:description":["Deep lamellar anterior keratoplasty (DALK) is a promising cornea transplant procedure, which mitigates the risks associated with the commonly used alternative penetrating keratoplasty. In DALK, a surgeon must insert a needle into the cornea to a precise depth and inject an air bubble to separate the stroma and endothelial layer. Optical coherence tomography (OCT) is used to help surgeons judge the needle insertion depth. However, the needle obscures the part of the cornea underneath it, making it difficult to estimate needle insertion depth. In this thesis, deep learning methods are explored for cornea and needle segmentation to automatically compute needle insertion depth from volumetric OCT scans. A simple post-processing step is applied to the mask produced by the deep neural networks to determine the coordinates of the top and bottom corneal boundaries and the needle tip. We compare the performance of 2D and 3D networks on this task. Our experiments consistently showed that all networks perform well on cornea segmentation in scans without a needle, and 3D networks perform better in scans with a needle. We also show that these deep learning methods perform better than existing graph-theory methods on this task and can segment in real-time when deployed using the NVIDIA TensorRT framework.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-05-01","The student, Harsh Agrawal, accepted the attached license on 2021-04-22 at 16:20.","The student, Harsh Agrawal, submitted this Thesis for approval on 2021-04-22 at 16:48.","This Thesis was approved for publication on 2021-04-26 at 11:33.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16168 on 2021-09-16 at 17:01:48","Made available in DSpace on 2021-09-17T02:34:13Z (GMT). No. of bitstreams: 2 AGRAWAL-THESIS-2021.pdf: 4131247 bytes, checksum: 5f69209391e2c155071baa2741ea6b5d (MD5) LICENSE.txt: 4210 bytes, checksum: 18eeeaf2dba94af3a7c57f8ed31e6122 (MD5) Previous issue date: 2021-04-26","Embargo set by: Seth Robbins for item 118473 Lift date: 2023-09-17T02:34:57Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/110630"],"dc:language":["en"],"dc:rights":["Copyright 2021 Harsh Agrawal"],"dc:subject":["deep learning","machine learning","image segmentation","OCT","cornea segmentation","tool tracking"],"dc:title":["Deep learning methods for real-time corneal and needle segmentation in volumetric OCT scans"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:52Z"}