SBIR Phase I: AI-assisted software for fast labeling of medical tomographic images

小型企业创新研究(SBIR)第一阶段项目的更广泛影响是从医学图像中提取新的有价值的信息，加快放射科医生对图像的解释，并改善患者的预后。识别图像中的关键特征的过程，即所谓的“标记”，是改进某些疾病诊断和管理的关键。这里提出的创新将大大降低标记数据集的成本，使人工智能(AI)算法的开发人员能够访问，并改善人工智能在医疗保健中的使用。这个小型企业创新研究(SBIR)第一阶段项目将应用机器学习算法来开发一个帮助手动标记医学断层图像的系统。这项拟议的研究将导致一种自适应的系统架构，该架构将演变为加速标记并增加标记数据量。此外，这项研究还将提高解剖结构边缘的标记准确性。例如，癌症治疗的手术切除需要准确地标记异常组织的边缘，以确保边缘干净，并将复发降至最低。同样，放射治疗计划需要准确地标记有风险的器官的边缘，以确保安全和良好的结果。由于其临床重要性，准确的人工标记歧义和复杂的形状是非常耗时的。建议的方法与现有方法的不同之处在于增加了一个额外的子系统，用于提高边缘标记的准确性。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to extract new valuable information from medical images, accelerate image interpretation by radiologists, and improve patient outcomes. The process of identifying key features in images, known as ``labeling'', is the key to improved diagnosis and management of certain conditions. The innovations proposed here will substantially lower the cost of labeled datasets, enabling access for developers of artificial intelligence (AI) algorithms and improving the use of AI in health care. This Small Business Innovation Research (SBIR) Phase I project will apply machine learning algorithms to develop a system for assisting in manual labeling of medical tomographic images. The proposed research will result in an adaptive system architecture that evolves to accelerate labeling and increase the volume of labeled data. Moreover, the research will increase labeling accuracy at the edges of anatomical structures. For instance, surgical resections for cancer treatment requires accurate labeling of the edges of abnormal tissue to ensure clean margins and minimal recurrence. Similarly, radiation therapy planning requires accurate labeling of the edges of organs at risk for safety and favorable outcomes. Due to its clinical importance, accurate manual labeling of ambiguities and sophisticated shapes is highly time-consuming. The proposed approach is differentiated from current methods by the inclusion of an additional subsystem for increasing the accuracy of edge labeling.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.