Clinicians face many challenges when diagnosing and treating breast cancer. These challenges include interpreting and co-locating information between different medical imaging modalities that are used to identify tumours and predicting where these tumours move to during different treatment procedures. We have developed a novel automated breast image analysis workflow that integrates state-of-the-art image processing and machine learning techniques, personalized three-dimensional biomechanical modelling and population-based statistical analysis to assist clinicians during breast cancer detection and treatment procedures. This paper summarizes our recent research to address the various technical and implementation challenges associated with creating a fully automated system. The workflow is applied to predict the repositioning of tumours from the prone position, where diagnostic magnetic resonance imaging is performed, to the supine position where treatment procedures are performed. We discuss our recent advances towards addressing challenges in identifying the mechanical properties of the breast and evaluating the accuracy of the biomechanical models. We also describe our progress in implementing a prototype of this workflow in clinical practice. Clinical adoption of these state-of-the-art modelling techniques has significant potential for reducing the number of misdiagnosed breast cancers, while also helping to improve the treatment of patients.

中文翻译：

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用于改善乳腺癌诊断和治疗的自动化计算生物力学工作流程。

在诊断和治疗乳腺癌时，临床医生面临许多挑战。这些挑战包括在用于识别肿瘤的不同医学成像方式之间解释和共同定位信息，以及预测在不同治疗过程中这些肿瘤将转移至何处。我们已经开发了一种新颖的自动化乳房图像分析工作流程，该流程集成了最新的图像处理和机器学习技术，个性化的三维生物力学建模以及基于人群的统计分析，以在乳腺癌检测和治疗过程中协助临床医生。本文总结了我们最近的研究，以解决与创建全自动系统相关的各种技术和实施挑战。该工作流程被用于预测从进行诊断性磁共振成像的俯卧位到进行治疗程序的仰卧位的肿瘤重定位。我们讨论了我们在解决识别乳房的机械特性和评估生物力学模型的准确性方面的挑战方面的最新进展。我们还将描述在临床实践中实现此工作流程原型的进展。这些最先进的建模技术的临床采用具有减少误诊乳腺癌的数量的巨大潜力，同时也有助于改善患者的治疗。我们讨论了我们在解决识别乳房的机械特性和评估生物力学模型的准确性方面的挑战方面的最新进展。我们还将描述在临床实践中实现此工作流程原型的进展。这些最新建模技术的临床采用具有减少误诊乳腺癌的数量的巨大潜力，同时也有助于改善患者的治疗。我们讨论了我们在解决识别乳房的机械特性和评估生物力学模型的准确性方面的挑战方面的最新进展。我们还将描述在临床实践中实现此工作流程原型的进展。这些最先进的建模技术的临床采用具有减少误诊乳腺癌的数量的巨大潜力，同时也有助于改善患者的治疗。