Automatic Pelvic Organ Delineation in Prostate Cancer Treatment

前列腺癌治疗中的自动盆腔器官描绘

• 批准号: 10221628
• 负责人: Jun Lian
• 金额: $ 36.22万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10221628
• 关键词: Address; Algorithms; Anatomy; Appearance; Area; Bladder; Cancer Hospital; Clinic; Clinical; Clinical Treatment; Data; Detection; Development; Dose; General Population; Goals; Hospitals; Image; Joints; Label; Lead; Learning; Machine Learning; Magnetic Resonance Imaging; Malignant neoplasm of prostate; Manuals; Methods; Modality; Modeling; Monitor; Multimodal Imaging; Normal Statistical Distribution; Organ; Patient Care; Patient Monitoring; Patient imaging; Patient-Focused Outcomes; Patients; Pelvis; Performance; Physicians; Population; Process; Prostate; Prostate Cancer therapy; Radiation therapy; Rectum; Sample Size; Shapes; Time; Tissues; Training; Update; Workload; X-Ray Computed Tomography; automated segmentation; base; deep learning; design; dosage; forest; image guided radiation therapy; imaging Segmentation; imaging modality; improved; innovation; learning strategy; male; novel; prostate radiotherapy; random forest; segmentation algorithm; soft tissue; success; therapy design; tool; treatment duration; treatment planning

Automatic Pelvic Organ Delineation in Prostate Cancer Treatment Abstract: Fast, reliable and accurate delineation of pelvic organs in the planning and treatment images is a long- standing, important and technically challenging problem. Its solution is highly required for state-of-the-art image-guided radiation therapy planning and treatment, as better treatment decisions rely on timely interpretation of anatomical information in the images. However, automatic segmentation in male pelvic regions is always difficult due to 1) low contrast between prostate and surrounding organs, and 2) possibly disparate shapes/appearances of bladder and rectum caused by tissue deformations. The goal of this project is to create a set of novel machine learning tools to achieve accurate, reliable and efficient delineation of important pelvic organs (e.g., prostate, bladder, and rectum) in different modalities (e.g., planning CT, treatment CT/CBCT, and MRI) for radiotherapy of prostate cancer. Planning CT. For automatic segmentation, landmark detection is often the first step in rapidly locating the target organs. Thus, in Aim 1, we will create a novel joint landmark detection approach, based on both random forests and auto-context model, to iteratively detect all landmarks and further coordinate their detection results for achieving more accurate and consistent landmark detection results. After roughly locating organs with the aid of those detected landmarks, the second step is to accurately segment boundaries of target organs in the planning CT. Accordingly, in Aim 2, we will create a set of learning methods to a) first simultaneously predict all pelvic organ boundaries in the planning CT with the regression forests trained by labeled training data, and b) then segment all pelvic organs jointly by deforming their respective shape models. In particular, to address the limitations of conventional deformable models in assuming simple Gaussian distributions for organ shapes, a novel hierarchical sparse shape composition approach will be developed to constrain shape models during deformable segmentation. Treatment CT/CBCT. During the course of serial radiation treatments, to quantitatively record and monitor the accumulated dose delivered to the patient, organs in the treatment image also need to be segmented. Although methods proposed in Aims 1-2 can be simply applied, as done by many conventional methods, this will lead to a) inconsistent landmark detection and b) inconsistent segmentations across different treatment days because of possible large shape/appearance changes. Accordingly, in Aim 3, we will create a novel self- learning mechanism to gradually learn and incorporate patient-specific information into both joint landmark detection and deformable segmentation steps from the increasingly acquired treatment images of patient. Thus, population data will gradually be replaced by the patient's own data to train personalized models. MRI. To guide pelvic organ segmentation in the planning CT, MRI is now often acquired for selected patients. To this end, in Aim 4, we will develop a) a prostate MRI segmentation method by using deep learning to learn MRI-specific features for guiding landmark detection and deformable segmentation as proposed in Aims 1-2; b) a novel collaborative MRI and CT segmentation algorithm for more accurate segmentation of planning CT. All our developed algorithms will be evaluated for their performance in clinical (treatment planning and delivery) workflow for 130 patients in UNC Cancer Hospital and also hospitals of our consultants. Benefit for Patient Care. Development of these segmentation tools will 1) dramatically accelerate the clinical workflow, 2) reduce workload (i.e., manual interaction time) for physicians, and 3) lead to better patient outcomes with reliable and accurate segmentations of target area and critical organs. Although these tools cannot replace the expertise of physicians, they can be of great assistance to physicians.

自动盆腔器官勾画在前列腺癌治疗中的应用 摘要： 在计划和治疗图像中快速、可靠和准确地勾画盆腔器官是一项长期的任务。 站稳脚跟，是一个重要的、具有技术挑战性的问题。它的解决方案是最先进的 影像引导的放射治疗计划和治疗，因为更好的治疗决策依赖于及时 对图像中的解剖信息进行解释。然而，男性骨盆区域的自动分割 这总是很困难，因为1)前列腺和周围器官之间的对比度很低，2)可能是不同的 由组织变形引起的膀胱和直肠的形状/外观。该项目的目标是创建 一套新颖的机器学习工具，实现准确、可靠和高效的重要骨盆勾画 不同方式的器官(如前列腺、膀胱和直肠)(如计划CT、治疗CT/CBCT、 和MRI)用于前列腺癌的放射治疗。 计划CT检查。对于自动分割，地标检测通常是快速定位 靶器官。因此，在目标1中，我们将创建一种新的基于两者的联合地标检测方法 随机森林和自动上下文模型，以迭代地检测所有地标并进一步协调其 检测结果，以实现更准确和一致的地标检测结果。在大致定位之后 第二步是利用检测到的标志点准确地分割目标的边界 在计划CT中的器官。因此，在目标2中，我们将创建一套学习方法来首先 用训练好的回归森林同时预测计划CT中的所有盆腔器官边界 标记的训练数据，以及b)然后通过变形它们各自的形状模型来联合分割所有盆腔器官。 特别是，为了解决传统可变形模型在假设简单高斯时的局限性 器官形状的分布，将开发一种新的分层稀疏形状合成方法来 在可变形分割期间约束形状模型。 治疗CT/CBCT。在连续放射治疗过程中，定量记录和监测 传递给患者的累积剂量，治疗图像中的器官也需要分割。 虽然AIMS 1-2中提出的方法可以简单地应用，就像许多传统方法所做的那样，但这 将导致a)不一致的地标检测和b)跨不同处理的不一致分割 天数，因为可能会有较大的形状/外观更改。因此，在目标3中，我们将创造一个新颖的自我- 逐步学习并将患者特定信息纳入双方联合里程碑的学习机制 从日益增长的患者治疗图像中检测和变形分割步骤。 因此，人口数据将逐渐被患者自己的数据取代，以训练个性化的模型。 核磁共振检查。为了在计划的CT中指导盆腔器官分割，现在通常选择MRI进行扫描 病人。为此，在目标4中，我们将开发一种基于深度分割的前列腺MRI分割方法 学习学习MRI特有的特征，以指导地标检测和可变形分割 在AIMS 1-2中提出了一种新的协作MRI和CT分割算法，以提高分割的精确度 计划CT的分割。 我们开发的所有算法将在临床上进行性能评估(治疗计划和 为北卡罗来纳大学肿瘤医院和我们的顾问医院的130名患者提供工作流程。 患者护理方面的福利。这些细分工具的开发将极大地加速 临床工作流程，2)减少医生的工作量(即手动交互时间)，以及3)带来更好的 通过可靠和准确的靶区和关键器官的分割，患者的预后。尽管这些 工具不能取代医生的专业知识，它们可以为医生提供很大的帮助。

项目成果

Pelvic Organ Segmentation Using Distinctive Curve Guided Fully Convolutional Networks.

使用独特曲线引导的全卷积网络进行盆腔器官分割

• DOI: 10.1109/tmi.2018.2867837
• 发表时间: 2019-03
• 期刊: IEEE transactions on medical imaging
• 影响因子: 10.6
• 作者: He K;Cao X;Shi Y;Nie D;Gao Y;Shen D
• 通讯作者: Shen D

In vivo MRI based prostate cancer localization with random forests and auto-context model.

• DOI: 10.1016/j.compmedimag.2016.02.001
• 发表时间: 2016-09
• 期刊: Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
• 作者: Qian C;Wang L;Gao Y;Yousuf A;Yang X;Oto A;Shen D
• 通讯作者: Shen D

CT Male Pelvic Organ Segmentation via Hybrid Loss Network With Incomplete Annotation.

• DOI: 10.1109/tmi.2020.2966389
• 发表时间: 2020-06
• 期刊: IEEE transactions on medical imaging
• 影响因子: 10.6
• 作者: Wang S;Nie D;Qu L;Shao Y;Lian J;Wang Q;Shen D
• 通讯作者: Shen D

Organ-aware CBCT enhancement via dual path learning for prostate cancer treatment.

• DOI: 10.1002/mp.16752
• 发表时间: 2023-09
• 期刊: Medical physics
• 影响因子: 3.8
• 作者: Xu Chen;Yunkui Pang;Sahar Ahmad;Trevor J Royce;Andrew Wang;Jun Lian;P. Yap

SEMI-SUPERVISED LEARNING FOR PELVIC MR IMAGE SEGMENTATION BASED ON MULTI-TASK RESIDUAL FULLY CONVOLUTIONAL NETWORKS.

基于多任务剩余完全卷积网络的骨盆MR图像分割的半监督学习。

• DOI: 10.1109/isbi.2018.8363713
• 发表时间: 2018-04
• 期刊: Proceedings. IEEE International Symposium on Biomedical Imaging
• 作者: Feng Z;Nie D;Wang L;Shen D
• 通讯作者: Shen D