# MicroBT: Centroid-Guided Synergistic Learning for Multi-Modal Micro Brain Tumor Segmentation and Counting

**Authors:** Zhenye Lou, Qing Xu, Stefanie Thust, Dorothee P. Auer, Zhen Chen, Wenting Duan  
**Published:** 10 Nov 2026  
**Links:** [arXiv](https://arxiv.org/abs/2611.13912) · [PDF](https://arxiv.org/pdf/2611.13912) · [Code](https://github.com/leonlou921/MicroBT) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2611.13912.html)

## Key numbers

- **7%**: localization precision improves by approximately 7% and counting estimation errors decrease by approximately 26%
- **26%**: localization precision improves by approximately 7% and counting estimation errors decrease by approximately 26%

## Abstract

Multi-modal brain tumor segmentation is essential for clinical diagnosis, radiotherapy planning, and longitudinal monitoring. While recent studies have achieved remarkable overall segmentation performance, they rely on overlap-based objectives that inherently favor large regions, causing sub-3 mm micro-lesions to be systematically suppressed during training. Moreover, existing pixel-level segmentation frameworks lack explicit instance-level modeling, leading to unreliable lesion counting that directly impacts clinical staging and therapeutic decisions. To address these challenges, we propose MicroBT, a unified multi-modal framework for micro brain tumor segmentation and counting. Our core insight is that a fundamental geometric property is inherently scale-invariant: regardless of size, each lesion possesses a well-defined centroid. Leveraging this, we first devise a centroid-anchored synergistic paradigm that utilizes predicted centroids as shared anchors to simultaneously condition dense segmentation and instance-level localization. To counteract the tumor size imbalance, we further devise a Volume-Calibrated Loss (VC-Loss) that adaptively amplifies the gradient contribution of micro-lesions, alongside a Size-Aware Curriculum Optimization (SACO) that stabilizes multi-task training by establishing global representations before progressively unlocking fine-grained localization. Extensive experiments demonstrate that MicroBT outperforms state-of-the-art methods, particularly on metastases, where micro-lesion (≤ 3 mm) localization precision improves by approximately 7% and counting estimation errors decrease by approximately 26%. The code is available at https://github.com/leonlou921/MicroBT.

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Notes written by AI from the paper, numbers copied from the abstract. Check the paper before you rely on them. From AI Papers Daily.