{"id":{"repo_id":"queens","oai_identifier":"oai:queensu.scholaris.ca:1974/35270"},"canonical_url":"https://search.dev.ndltd.org/etd/queens/oai:queensu.scholaris.ca:1974/35270","repository":{"repo_id":"queens","name":"Queens University","base_url":"https://qspace.library.queensu.ca/server/oai/request"},"display":{"title":"A Depth-Guided Annotation Tool for B-Line Quantification in Lung Ultrasound","abstract":"Pulmonary congestion is a critical and common complication of congestive heart failure, requiring timely and accurate monitoring to guide clinical decision-making. Lung ultrasound (LUS) has emerged as a promising point-of-care tool for assessing pulmonary fluid status due to its portability, safety, and sensitivity. However, current LUS interpretation methods, particularly manual B-line counting, are highly subjective and suffer from substantial inter- and intra-observer variability. This variability limits reproducibility, hampers clinical integration, and challenges the development of robust AI models for LUS analysis. This thesis presents the design, implementation, and evaluation of AnnotateUltrasound, a novel open-source module for structured LUS annotation within the 3D Slicer platform. The tool introduces a standardized sector-based annotation schema and a visual depth guide to reduce subjectivity in pleural B-line coverage estimation. A human-centered design process, informed by iterative clinical feedback, shaped a user-friendly interface with structured annotation, efficient navigation, and support for multi-rater workflows. Empirical evaluation involved a user study with 18 participants from clinical and non-clinical backgrounds. Results show that the depth guide reduced inter-rater variability (mean MAD: 0.063 to 0.034) and improved overall inter-rater agreement. Intra-rater consistency also improved with the guide (correlation r = 0.85 to 0.92), supporting the guide’s role in enhancing reproducibility. Participants reported high usability (mean SUS score: 83.2) and reduced cognitive workload (NASA-TLX). Qualitative feedback further highlighted the tool’s utility as both a reproducible annotation platform and an effective educational aid. The AnnotateUltrasound module is already in use by clinicians, including researchers at Harvard-affiliated institutions, to support large-scale dataset curation, gold-standard adjudication, and AI model development. This tool addresses a critical gap in structured LUS annotation workflows by enabling reproducible, sector-based quantification of B-lines and pleural features. Its AI-ready design lays the groundwork for integrating automated models into diagnostic and annotation pipelines, ultimately supporting reproducible lung ultrasound analysis in heart failure care and beyond.","abstract_html":"Pulmonary congestion is a critical and common complication of congestive heart failure, requiring timely and accurate monitoring to guide clinical decision-making. Lung ultrasound (LUS) has emerged as a promising point-of-care tool for assessing pulmonary fluid status due to its portability, safety, and sensitivity. However, current LUS interpretation methods, particularly manual B-line counting, are highly subjective and suffer from substantial inter- and intra-observer variability. This variability limits reproducibility, hampers clinical integration, and challenges the development of robust AI models for LUS analysis. This thesis presents the design, implementation, and evaluation of AnnotateUltrasound, a novel open-source module for structured LUS annotation within the 3D Slicer platform. The tool introduces a standardized sector-based annotation schema and a visual depth guide to reduce subjectivity in pleural B-line coverage estimation. A human-centered design process, informed by iterative clinical feedback, shaped a user-friendly interface with structured annotation, efficient navigation, and support for multi-rater workflows. Empirical evaluation involved a user study with 18 participants from clinical and non-clinical backgrounds. Results show that the depth guide reduced inter-rater variability (mean MAD: 0.063 to 0.034) and improved overall inter-rater agreement. Intra-rater consistency also improved with the guide (correlation r = 0.85 to 0.92), supporting the guide’s role in enhancing reproducibility. Participants reported high usability (mean SUS score: 83.2) and reduced cognitive workload (NASA-TLX). Qualitative feedback further highlighted the tool’s utility as both a reproducible annotation platform and an effective educational aid. The AnnotateUltrasound module is already in use by clinicians, including researchers at Harvard-affiliated institutions, to support large-scale dataset curation, gold-standard adjudication, and AI model development. This tool addresses a critical gap in structured LUS annotation workflows by enabling reproducible, sector-based quantification of B-lines and pleural features. Its AI-ready design lays the groundwork for integrating automated models into diagnostic and annotation pipelines, ultimately supporting reproducible lung ultrasound analysis in heart failure care and beyond.","abstract_has_math":false,"creators":["Kesibi, Maha"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Computing","school":null,"contributors":[],"advisors":["Ungi, Tamas","Mousavi, Parvin","Fichtinger, Gabor"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-09-26","date_published":"2025-09-26","updated_at":"2026-07-27T20:35:29Z","subjects":["Lung ultrasound","Annotation","Open-source","Artificial intelligence","Observer variability","Human-centered design"],"languages":["eng"],"rights":["Attribution-NonCommercial-NoDerivatives 4.0 International"],"rights_urls":["http://creativecommons.org/licenses/by-nc-nd/4.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1974/35270","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.department","label":"Department","values":["Computing"]},{"key":"dc:contributor.supervisor","label":"Supervisor","values":["Ungi, Tamas","Mousavi, Parvin","Fichtinger, Gabor"]},{"key":"dc:creator","label":"Author","values":["Kesibi, Maha"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-09-26T13:20:33Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-09-26T13:20:33Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-09-26"]},{"key":"dc:type","label":"Dc Type","values":["thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Lung ultrasound","Annotation","Open-source","Artificial intelligence","Observer variability","Human-centered design"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Attribution-NonCommercial-NoDerivatives 4.0 International"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://creativecommons.org/licenses/by-nc-nd/4.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1974/35270"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Pulmonary congestion is a critical and common complication of congestive heart failure, requiring timely and accurate monitoring to guide clinical decision-making. Lung ultrasound (LUS) has emerged as a promising point-of-care tool for assessing pulmonary fluid status due to its portability, safety, and sensitivity. However, current LUS interpretation methods, particularly manual B-line counting, are highly subjective and suffer from substantial inter- and intra-observer variability. This variability limits reproducibility, hampers clinical integration, and challenges the development of robust AI models for LUS analysis. This thesis presents the design, implementation, and evaluation of AnnotateUltrasound, a novel open-source module for structured LUS annotation within the 3D Slicer platform. The tool introduces a standardized sector-based annotation schema and a visual depth guide to reduce subjectivity in pleural B-line coverage estimation. A human-centered design process, informed by iterative clinical feedback, shaped a user-friendly interface with structured annotation, efficient navigation, and support for multi-rater workflows. Empirical evaluation involved a user study with 18 participants from clinical and non-clinical backgrounds. Results show that the depth guide reduced inter-rater variability (mean MAD: 0.063 to 0.034) and improved overall inter-rater agreement. Intra-rater consistency also improved with the guide (correlation r = 0.85 to 0.92), supporting the guide’s role in enhancing reproducibility. Participants reported high usability (mean SUS score: 83.2) and reduced cognitive workload (NASA-TLX). Qualitative feedback further highlighted the tool’s utility as both a reproducible annotation platform and an effective educational aid. The AnnotateUltrasound module is already in use by clinicians, including researchers at Harvard-affiliated institutions, to support large-scale dataset curation, gold-standard adjudication, and AI model development. This tool addresses a critical gap in structured LUS annotation workflows by enabling reproducible, sector-based quantification of B-lines and pleural features. Its AI-ready design lays the groundwork for integrating automated models into diagnostic and annotation pipelines, ultimately supporting reproducible lung ultrasound analysis in heart failure care and beyond."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Sc."]},{"key":"dc:title","label":"Title","values":["A Depth-Guided Annotation Tool for B-Line Quantification in Lung Ultrasound"]}]}],"canonical_facts":{"dc:contributor.department":["Computing"],"dc:contributor.supervisor":["Ungi, Tamas","Mousavi, Parvin","Fichtinger, Gabor"],"dc:creator":["Kesibi, Maha"],"dc:date.accessioned":["2025-09-26T13:20:33Z"],"dc:date.available":["2025-09-26T13:20:33Z"],"dc:date.issued":["2025-09-26"],"dc:description.abstract":["Pulmonary congestion is a critical and common complication of congestive heart failure, requiring timely and accurate monitoring to guide clinical decision-making. Lung ultrasound (LUS) has emerged as a promising point-of-care tool for assessing pulmonary fluid status due to its portability, safety, and sensitivity. However, current LUS interpretation methods, particularly manual B-line counting, are highly subjective and suffer from substantial inter- and intra-observer variability. This variability limits reproducibility, hampers clinical integration, and challenges the development of robust AI models for LUS analysis. This thesis presents the design, implementation, and evaluation of AnnotateUltrasound, a novel open-source module for structured LUS annotation within the 3D Slicer platform. The tool introduces a standardized sector-based annotation schema and a visual depth guide to reduce subjectivity in pleural B-line coverage estimation. A human-centered design process, informed by iterative clinical feedback, shaped a user-friendly interface with structured annotation, efficient navigation, and support for multi-rater workflows. Empirical evaluation involved a user study with 18 participants from clinical and non-clinical backgrounds. Results show that the depth guide reduced inter-rater variability (mean MAD: 0.063 to 0.034) and improved overall inter-rater agreement. Intra-rater consistency also improved with the guide (correlation r = 0.85 to 0.92), supporting the guide’s role in enhancing reproducibility. Participants reported high usability (mean SUS score: 83.2) and reduced cognitive workload (NASA-TLX). Qualitative feedback further highlighted the tool’s utility as both a reproducible annotation platform and an effective educational aid. The AnnotateUltrasound module is already in use by clinicians, including researchers at Harvard-affiliated institutions, to support large-scale dataset curation, gold-standard adjudication, and AI model development. This tool addresses a critical gap in structured LUS annotation workflows by enabling reproducible, sector-based quantification of B-lines and pleural features. Its AI-ready design lays the groundwork for integrating automated models into diagnostic and annotation pipelines, ultimately supporting reproducible lung ultrasound analysis in heart failure care and beyond."],"dc:description.degree":["M.Sc."],"dc:identifier.uri":["https://hdl.handle.net/1974/35270"],"dc:language.iso":["eng"],"dc:rights":["Attribution-NonCommercial-NoDerivatives 4.0 International"],"dc:rights.uri":["http://creativecommons.org/licenses/by-nc-nd/4.0/"],"dc:subject":["Lung ultrasound","Annotation","Open-source","Artificial intelligence","Observer variability","Human-centered design"],"dc:title":["A Depth-Guided Annotation Tool for B-Line Quantification in Lung Ultrasound"],"dc:type":["thesis"]},"updated_at":"2026-07-27T20:35:29Z"}