BG-YOLO11s: Boundary-Guided YOLO11 with Bézier Contour Augmentation for Brain Tumour Segmentation in T1-CE MRI.

Overview

1. Department of Computer Engineering, Kırıkkale University, Kırıkkale 71450, Türkiye
2. Department of Electronics and Information Technologies, Faculty of Architecture and Engineering, Nakhchivan State University, AZ 7012 Nakhchivan, Azerbaijan
3. Department of Computer Engineering, İstanbul Topkapı University, İstanbul 34394, Türkiye
4. Department of Computer Engineering, Iğdır University, Iğdır 76000, Türkiye; (F.Ö.); (I.P.)
5. Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Fenerbahce University, Istanbul 34758, Türkiye

Abstract

Background/Objectives: Accurate delineation of brain tumours on contrast-enhanced MRI remains difficult because lesions can be small, irregular, and weakly separated from adjacent tissue. This study developed BG-YOLO11s, a boundary-guided single-stage instance-segmentation model for T1 contrast-enhanced MRI. Methods: The public Figshare/Cheng dataset, comprising 3064 slices from 233 patients, was converted to YOLO polygon annotations and evaluated using a fixed 70/15/15 image-level split (2144/459/461 slices). Because patient identifiers were not retained in the exported image-and-polygon data, the split was not guaranteed to be patient-disjoint. Bézier Contour Augmentation generated two contour-perturbed training samples per original slice while leaving validation and test data unchanged. BG-YOLO11s extended YOLO11s-seg with dilated context aggregation in the backbone, boundary-enhanced feature fusion in the neck, and a prototype refinement module with differentiable boundary-aware supervision in the segmentation head. Results: In a single run on the held-out image-level test split, BG-YOLO11s achieved 92.4% precision, 88.7% recall, 94.6% mask, 68.9% –95, and 86.5% IoU. Relative to YOLO11s-seg, the corresponding gains were 3.8 points in, 5.8 points in –95, and 4.1 points in IoU. A progressive ablation produced incremental gains along the fixed module-addition sequence, but it did not isolate all component interactions or quantify run-to-run uncertainty. Conclusions: BG-YOLO11s improved single-run mask-overlap estimates under the present image-level benchmark. Patient-disjoint retraining, repeated-seed statistics, boundary-specific metrics, complete failure pattern auditing, and external multi-sequence validation are required before broader clinical or deployment claims can be made

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

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Data

Datasets cited

• figshare:1512427, at figshare; found in “Data Availability Statement”

Data Availability Statement

The Figshare brain tumour dataset analysed in this study is publicly available at: https://doi.org/10.6084/m9.figshare.1512427.v5. The source code, model definitions, executable training and evaluation configurations, and trained weights are available from the corresponding author upon reasonable request. The MRI data are not redistributed—users should obtain them from the original Figshare repository.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 8 keywords, 1 funder, 16 references.

Cite

This paper

Yurdakul, M., Zeynalov, J., Ersoy, M., Özger, F., & Pacal, I. (2026). BG-YOLO11s: Boundary-Guided YOLO11 with Bézier Contour Augmentation for Brain Tumour Segmentation in T1-CE MRI. Diagnostics (Basel, Switzerland), 16(15), 2407. https://doi.org/10.3390/diagnostics16152407

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