A clinical tool for automated detection and delineation of intracranial metastases from MRI

随着多发性颅内转移瘤的治疗迅速发展，对 发现和描绘大量潜在的非常小的转移灶 脑部的3D磁共振成像(MRI)正在急剧增加。人工智能 (AI)系统可以帮助放射科医生和放射肿瘤科医生在以下方面发挥作用 多发性脑转移瘤的治疗。为了回应PAR-20- 我们已经组建了一个学术和产业合作伙伴关系，其中包括来自三个国家的研究人员 学术机构和工业人工智能团队开发、翻译和验证人工智能系统，以 解决这个临床上未解决的问题。在本提案中，一种基于 包含分割的多尺度3D全卷积单阶段目标探测器 将对头部进行优化和调查。培训和测试数据将从临床提供 经放射外科治疗的多发性小转移瘤患者的影像 学术中心，由专家管理，并通过增加真实的合成损伤来增强 注入到图像中。该网络的临床效用将被调查其协助的能力。 A)放射科医生准确有效地检测多个转移瘤；b)放射 肿瘤学家在勾画多个转移灶以支持治疗策略选择方面的作用 以及治疗方案的制定。

As the management of multiple intracranial metastases is rapidly evolving, the demands for detection and delineation of a large number of potentially very small metastatic lesions in the brain on 3D magnetic resonance images (MRI) are increasing dramatically. Artificial intelligence (AI) systems can assist both the radiologist as well as radiation oncologist in their roles in management of patients with multiple tumors metastatic to the brain. In response to PAR-20- 155, we have assembled an academic-industrial partnership including investigators from three academic institutes and an industrial AI team to develop, translate and validate AI systems to address this unmet clinical question. In this proposal, a neural network system based upon multiple scale 3D fully convolutional one-stage objective detectors containing segmentation heads will be optimized and investigated. Training and testing data will be provided from clinical images of patients treated with radiosurgery to multiple small metastases acquired from three academic centers, curated by experts, and augmented by addition of realistic synthetic lesions injected into images. The clinical utility of the network will be investigated for its ability to assist a) radiologists in detecting multiple metastases accurately and efficiently, and b) radiation oncologists in delineating multiple metastatic lesions to support selection of therapeutic strategies and planning of treatments.