A Depth-guided Annotation Tool for B-line Quantification in Lung Ultrasound
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| Publicado en: | ProQuest Dissertations and Theses (2025) |
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| Acceso en línea: | Citation/Abstract Full Text - PDF |
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| 045 | 2 | |b d20250101 |b d20251231 | |
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| 100 | 1 | |a Kesibi, Maha | |
| 245 | 1 | |a A Depth-guided Annotation Tool for B-line Quantification in Lung Ultrasound | |
| 260 | |b ProQuest Dissertations & Theses |c 2025 | ||
| 513 | |a Dissertation/Thesis | ||
| 520 | 3 | |a 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 subjec-tive 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 AnnotateUl-trasound, 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 → 0.034) and improved overall inter-rater agreement.Intra-rater consistency also improved with the guide (correlation r = 0.85 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). Qual-itative 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 re-searchers 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. | |
| 653 | |a User interface | ||
| 653 | |a Usability | ||
| 653 | |a Deep learning | ||
| 653 | |a User experience | ||
| 653 | |a User feedback | ||
| 653 | |a Medical imaging | ||
| 653 | |a Geometry | ||
| 653 | |a Ultrasonic imaging | ||
| 653 | |a Reproducibility | ||
| 653 | |a Keyboards | ||
| 653 | |a Artificial intelligence | ||
| 773 | 0 | |t ProQuest Dissertations and Theses |g (2025) | |
| 786 | 0 | |d ProQuest |t ProQuest Dissertations & Theses Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3283373991/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3283373991/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |