  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.
