Deep LOGISMOS

摘要： 这是一个项目的竞争性延续，该项目已经产生了高度灵活、准确和 广泛适用的混合深度学习图优化上下文的Deep LOGISMOS框架-- 感知n维医学图像分割。在这个新的研究阶段，我们将重新制定 深LOGISMOS将如何受益于从训练数据中学习的拓扑和形状知识， 细分质量控制将如何提高分析效率，以及总体性能收益如何 将实现始终提供可靠的定量图像分析，这是在 临床护理。 为了推动本研究项目进入新的阶段，我们假设：相关的改进 训练数据集中的信息密度，并提高了对象的稳健性和正确性 拓扑和形状估计，以及通过自动化的智能适量交互(JEI)指导 对分割质量的评估将在临床上普遍带来可接受的定量分析 跨不同应用领域的常规获取的、复杂的、诊断性质量的医学图像。 拟议的研究项目汇集了多项重大创新。它建立在坚实的科学基础上 前提，在已经取得的成果的基础上，扩展深度学习的最新水平，并解决 有效地形成紧凑的高质量的带标注数据的训练集的刻录问题。学到的 深度LOGISMOS学习模型中将新加入对象拓扑和形状模式。它 在具有挑战性的案例中提高图像分析的正确性和性能，提供新的见解 使用自动图像分割质量评估来指导可选的JEI过程和 提高他们的效率，并进一步扩展我们的 接近。 我们将实现以下具体目标： 1.开发新颖、健壮的深度学习方法，以学习和灌输全面的拓扑和 深LOGISMOS的形状感知，同时保持其混合DL图优化原则。 2.通过智能的恰到好处的互动，最大限度地提升Deep LOGISMOS+JEI的细分成功率。 3.通过使用自动质量控制来开发构建训练集的有效方法。 4.在医疗保健相关应用中，展示Deep LOGISMOS+JEI带来的高成功率 全自动分析，允许在需要时进行高效的JEI判决，并改进分割 与最先进的分割技术相比较的性能。 下一代Deep LOGISMOS将带来广泛可用的临床常规量化 图像，极大地提升了基于图像的信息在未来精确医学中的影响。

Abstract: This is a competitive continuation of a project that already yielded the highly flexible, accurate, and broadly applicable Deep LOGISMOS framework for hybrid deep-learning–graph optimization context- aware n-dimensional medical image segmentation. In this new research phase, we will reformulate the way how Deep LOGISMOS will benefit from topology and shape knowledge learned from training data, how segmentation quality control will increase analysis efficiency, and how an overall performance gain will be reached to always deliver reliable quantitative image analysis that is necessary for adoption in clinical care. To stimulate a new phase of this research project, we hypothesize that: Improvements of relevant information density in training datasets, combined with increased robustness and correctness of object topology and shape estimates, and smart Just-Enough Interaction (JEI) guidance via automated assessment of segmentation quality will universally bring clinically acceptable quantitative analyses in routinely acquired, complex, diagnostic-quality medical images across diverse application areas. The proposed research project brings together multiple major innovations. It is based on a solid scientific premise, builds on already achieved results, extends state-of-the-art of deep learning, and addresses the burning question of efficiently forming compact high-quality training sets of annotated data. Learned object topology and shape patterns will be newly incorporated in Deep LOGISMOS learning models. It improves image analysis correctness and performance in challenging cases, provides new insights in use of automated image segmentation quality assessment to guide the optional JEI processes and increase their efficiency, and further extends the translational and clinical utility and significance of our approach. We will fulfill the following specific aims: 1. Develop novel, robust deep-learning approaches to learn and instill comprehensive topology and shape awareness in Deep LOGISMOS while maintaining its hybrid DL–graph optimization principles. 2. Maximize segmentation success of Deep LOGISMOS + JEI via smart Just-Enough Interaction. 3. Develop efficient approaches for constructing training sets by employing automated quality control. 4. In healthcare-relevant applications, demonstrate that Deep LOGISMOS + JEI leads to high success of fully automated analyses, allows efficient JEI adjudication if needed, and improves segmentation performance in comparison with state-of-the-art segmentation techniques. The next-generation Deep LOGISMOS will bring forth broadly available routine quantification of clinical images, significantly elevating the impact of image-based information in tomorrow’s precision medicine.