<![CDATA[Purpose: Since glioma is the most aggressive and infiltrative type of brain tumor, its detection in magnetic resonance imaging (MRI) is especially difficult. Despite the excellent overall accuracy for brain tumor detection with YOLOv8-based object detection, the glioma-specific performance is limited owing to ambiguity of tumor boundaries. This work seeks to elucidate if boundary-aware learning can enhance glioma detection beyond typical bounding box–based approaches. Methods/Study design/approach: This study focuses exclusively on glioma detection using the Cheng brain tumor MRI dataset. YOLOv8 is used as the baseline detector, and boundary-aware learning is implemented through segmentation supervision using YOLOv8-Seg by leveraging pixel-level tumor masks. All the experiments are done in a standardized training environment to allow fair and unbiased comparison. Result/Findings: Experimental performance demonstrates the saturation of detection with YOLOv8 on glioma, irrespective of architectural and loss-level optimizations. Nonetheless, supervision on segmentation results in better modeling of glioma’s boundaries and leads to more informative localization responses, especially for infiltrative tumor areas. Novelty/Originality/Value: Unlike the other works that are based on augmentation of the data and performance of better detection, this work has devised a glioma-centric design, and shows bounding box-based detection is insufficient. This work highlights the need for considering boundary aware learning applying the supervision of segmentation in the automated glioma detection system, which can improve the reliability and interpretability of the system.]]>
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