Dual bounding box for medical image segmentation

Alshewaier, Hateef, Qin, Yipeng ORCID: https://orcid.org/0000-0002-1551-9126 and Sun, Xianfang ORCID: https://orcid.org/0000-0002-6114-0766 2026. Dual bounding box for medical image segmentation. Presented at: The 6th International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2025), London,UK, 19-21 November 2025. Lecture Notes in Electrical Engineering. Springer Science Business Media, Item availability restricted.

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Abstract

Segmentation in medical imaging is crucial for accurately identifying anatomical structures and pathological areas and is essential for precise diagnosis and treatment planning. Although bounding boxes have been widely used as a weak labeling method for training segmentation models due to their annotation efficiency, we propose a novel dual bounding box approach. This method employs two bounding boxes per class: an inner box focusing on the core target and an outer box capturing peripheral regions and contextual information. Unlike traditional single-bounding box methods, the dual-box approach provides enhanced guidance to the model, enabling it to incrementally refine segmentation boundaries and address challenges such as boundary ambiguity. Importantly, this method does not require significant additional annotation effort, as both bounding boxes can be easily derived during the annotation process. By combining weak labeling efficiency with improved segmentation accuracy, our dual-bounding box approach offers a cost-effective solution for medical image segmentation, providing a practical compromise between annotation ease and clinical utility.

Item Type:	Conference or Workshop Item - published (Paper)
Status:	In Press
Schools:	Schools > Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher:	Springer Science Business Media
ISSN:	1876-1100
Date of First Compliant Deposit:	17 February 2026
Date of Acceptance:	5 September 2025
Last Modified:	17 Feb 2026 17:01
URI:	https://orca.cardiff.ac.uk/id/eprint/184711

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