arXiv:2510.14383v2 Announce Type: replace 
Abstract: Accurate brain tumor segmentation is significant for clinical diagnosis and treatment but remains challenging due to tumor heterogeneity. Mamba-based State Space Models have demonstrated promising performance. However, despite their computational efficiency over other neural architectures, they incur considerable overhead for this task due to their sequential feature computation across multiple spatial axes. Moreover, their robustness across diverse BraTS data partitions remains largely unexplored, leaving a critical gap in reliable evaluation. To address this, we first propose a dual-resolution bi-directional Mamba (DRBD-Mamba), an efficient 3D segmentation model that captures multi-scale long-range dependencies with minimal computational overhead. We leverage a space-filling curve to preserve spatial locality during 3D-to-1D feature mapping, thereby reducing reliance on computationally expensive multi-axial feature scans. To enrich feature representation, we propose a gated fusion module that adaptively integrates forward and reverse contexts, along with a quantization block that improves robustness. We further propose five systematic folds on BraTS2023 for rigorous evaluation of segmentation techniques under diverse conditions and present analysis of common failure scenarios. On the 20% test set used by recent methods, our model achieves Dice improvements of 0.10% for whole tumor, 1.75% for tumor core, and 0.93% for enhancing tumor. Evaluations on the proposed systematic folds demonstrate that our model maintains competitive whole tumor accuracy while achieving clear average Dice gains of 1.16% for tumor core and 1.68% for enhancing tumor over existing state-of-the-art. Furthermore, our model achieves a 15x efficiency improvement while maintaining high segmentation accuracy, highlighting its robustness and computational advantage over existing methods.

يمثل التطور الأخير لـ DRBD-Mamba في تقسيم أورام الدماغ تقدمًا كبيرًا في التصوير الطبي. تتناول هذه الطريقة المبتكرة التحديات التي تطرحها تباينات الأورام، حيث تقدم دقة محسّنة للتشخيص والعلاج السريري. من خلال الاستفادة من نماذج الفضاء الحالة المستندة إلى Mamba، حقق الباحثون نتائج واعدة، مما يعزز الكفاءة الحسابية أثناء معالجة تعقيدات حساب الميزات المتسلسلة. لا يساعد هذا التقدم فقط في تحسين نتائج المرضى، بل يمهد أيضًا الطريق للابتكارات المستقبلية في هذا المجال.

El reciente desarrollo de DRBD-Mamba para la segmentación de tumores cerebrales marca un avance significativo en la imagen médica. Este enfoque innovador aborda los desafíos que plantea la heterogeneidad de los tumores, ofreciendo una mayor precisión para el diagnóstico clínico y el tratamiento. Al aprovechar los Modelos de Espacio de Estado basados en Mamba, los investigadores han logrado resultados prometedores, mejorando la eficiencia computacional mientras abordan las complejidades del cálculo secuencial de características. Este avance no solo ayuda a mejorar los resultados de los pacientes, sino que también allana el camino para futuras innovaciones en el campo.

Le développement récent de DRBD-Mamba pour la segmentation des tumeurs cérébrales représente une avancée significative dans l'imagerie médicale. Cette approche innovante répond aux défis posés par l'hétérogénéité des tumeurs, offrant une précision améliorée pour le diagnostic clinique et le traitement. En s'appuyant sur des modèles d'état basés sur Mamba, les chercheurs ont obtenu des résultats prometteurs, améliorant l'efficacité computationnelle tout en s'attaquant aux complexités du calcul des caractéristiques séquentielles. Cette avancée non seulement aide à de meilleurs résultats pour les patients, mais ouvre également la voie à de futures innovations dans le domaine.

The recent development of DRBD-Mamba for brain tumor segmentation marks a significant advancement in medical imaging. This innovative approach addresses the challenges posed by tumor heterogeneity, offering improved accuracy for clinical diagnosis and treatment. By leveraging Mamba-based State Space Models, researchers have achieved promising results, enhancing computational efficiency while tackling the complexities of sequential feature computation. This breakthrough not only aids in better patient outcomes but also paves the way for future innovations in the field.

DRBD-Mamba for Robust and Efficient Brain Tumor Segmentation with Analytical Insights

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DRBD-Mamba for Robust and Efficient Brain Tumor Segmentation with Analytical Insights

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