Kim Matthew, Wang Jen-Yeu, Lu Weiguo, Jiang Hao, Stojadinovic Strahinja, Wardak Zabi, Dan Tu, Timmerman Robert, Wang Lei, Chuang Cynthia, Szalkowski Gregory, Liu Lianli, Pollom Erqi, Rahimy Elham, Soltys Scott, Chen Mingli, Gu Xuejun
Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA.
Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX 75390, USA.
Bioengineering (Basel). 2024 May 3;11(5):454. doi: 10.3390/bioengineering11050454.
Detection and segmentation of brain metastases (BMs) play a pivotal role in diagnosis, treatment planning, and follow-up evaluations for effective BM management. Given the rising prevalence of BM cases and its predominantly multiple onsets, automated segmentation is becoming necessary in stereotactic radiosurgery. It not only alleviates the clinician's manual workload and improves clinical workflow efficiency but also ensures treatment safety, ultimately improving patient care. Recent strides in machine learning, particularly in deep learning (DL), have revolutionized medical image segmentation, achieving state-of-the-art results. This review aims to analyze auto-segmentation strategies, characterize the utilized data, and assess the performance of cutting-edge BM segmentation methodologies. Additionally, we delve into the challenges confronting BM segmentation and share insights gleaned from our algorithmic and clinical implementation experiences.
脑转移瘤（BMs）的检测与分割在有效管理BMs的诊断、治疗规划及随访评估中发挥着关键作用。鉴于BM病例的患病率不断上升且多为多发病灶，在立体定向放射外科中，自动分割变得十分必要。它不仅减轻了临床医生的手动工作量，提高了临床工作流程效率，还确保了治疗安全性，最终改善了患者护理。机器学习，尤其是深度学习（DL）的最新进展，彻底改变了医学图像分割，取得了领先成果。本综述旨在分析自动分割策略，描述所使用的数据特征，并评估前沿BM分割方法的性能。此外，我们深入探讨了BM分割面临的挑战，并分享了从我们的算法和临床实施经验中获得的见解。
