Brain metastases (BMs) are the most frequently occurring brain tumors. The treatment of patients having multiple BMs with stereo tactic radiosurgery necessitates accurate localization of the metastases. Neural networks can assist in this time-consuming and costly task that is typically performed by human experts. Particularly challenging is the detection of small lesions since they are often underrepresented in exist ing approaches. Yet, lesion detection is equally important for all sizes. In this work, we develop an ensemble of neural networks explicitly fo cused on detecting and segmenting small BMs. To accomplish this task, we trained several neural networks focusing on individual aspects of the BM segmentation problem: We use blob loss that specifically addresses the imbalance of lesion instances in terms of size and texture and is, therefore, not biased towards larger lesions. In addition, a model using a subtraction sequence between the T1 and T1 contrast-enhanced sequence focuses on low-contrast lesions. Furthermore, we train additional models only on small lesions. Our experiments demonstrate the utility of the ad ditional blob loss and the subtraction sequence. However, including the specialized small lesion models in the ensemble deteriorates segmentation results. We also find domain-knowledge-inspired postprocessing steps to drastically increase our performance in most experiments. Our approach enables us to submit a competitive challenge entry to the ASNR-MICCAI BraTS Brain Metastasis Challenge 2023.

翻译：脑转移瘤是最常见的脑肿瘤。采用立体定向放射外科治疗多发脑转移瘤患者时，需要对转移灶进行精确定位。神经网络可辅助完成这项通常由人类专家执行的高耗时且成本高昂的任务。其中，小病灶的检测尤为困难，因为现有方法往往对其关注不足。然而，病灶检测对所有尺寸而言均同等重要。本研究开发了一个专门针对小脑转移瘤检测与分割的神经网络集成模型。为此，我们训练了多个聚焦于脑转移瘤分割问题不同方面的神经网络：采用斑块损失函数，该函数专门处理病灶实例在尺寸和纹理上的不平衡问题，因此不会对大病灶产生偏向；同时，利用T1与T1增强序列间的减影序列构建模型，专注于低对比度病灶；此外，我们仅基于小病灶额外训练了模型。实验表明，引入斑块损失函数和减影序列具有显著效用。然而，将专攻小病灶的模型纳入集成体系反而导致分割效果下降。我们还发现，基于领域知识的后处理步骤可在多数实验中大幅提升性能。我们的方法使其在2023年ASNR-MICCAI脑转移瘤BraTS挑战赛中提交了具有竞争力的参赛方案。