Testing and improvement of a prototype application for lesion classification and data creation for computerized image analysis for lesion detection in multiple myeloma

测试和改进用于多发性骨髓瘤病变检测的计算机图像分析的病变分类和数据创建的原型应用程序

• 批准号: 286489910
• 负责人: Dr. Andrea Fränzle
• 金额: --
• 依托单位: Abteilung Medizinische Physik in der Strahlentherapie
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2015
• 资助国家: 德国
• 起止时间: 2014-12-31 至 2016-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/286489910?language=en
• 关键词: Testing; improvement; prototype; application; lesion

Multiple Myeloma (MM) is a systemic tumor disease in the bone marrow, which affects the skeleton. It causes multiple tumors in bones, but also osteolytic lesions, resulting in diffuse or focal dissolution of bone tissue. These lesions cause painful bone destruction and fractures. Currently, neither overall tumor mass nor the degree of skeletal changes can be quantified. Therefor the segmentation of every single lesion is needed. The large amount of lesions and the examination of the whole skeleton impede manual segmentation of all lesions in the clinician's daily routine. Delineation of tumors and osteolytic lesions requires multiple modalities because lesions show different characteristics in different modalities. Only by using automatic methods this segmentation task can be performed in a reasonable way. Currently, no automatic solutions for this problem are available. Knowing overall tumor volume and quantification of skeletal status might lead to more accurate staging and therapy monitoring. An important step for this analysis is the segmentation of single bones. Segmentation of single bones is also important in other applications. For the analysis of new treatment strategies in MM, for the irradiation of bones e.g., the volume of the affected hematopoietic bone marrow is relevant. Therefor the segmentation of the bones is necessary. Furthermore, segmentation of single bones is highly relevant for other applications in oncology and radiotherapy, since bones give important information for motion analysis and motion compensation during therapy. In this project, the data basis is to be created for further automation of image analysis in MM. An already developed prototype for lesion classification for lesion segmentation based on multimodal features was exemplarily tested on 4 MM patients. Aim of the project is the analysis and improvement of this prototype application. The application is to be trained and tested based on a larger data sample. Therefor the creation of training data is needed, which requires manual segmentation of lesions and single bones in whole body images. Manual segmentation of lesions is essential since it is the base for a pattern recognition method that uses multimodal features and learns characteristics of lesions and healthy tissues and should later be able to detect lesions in new whole body images. Manual bone segmentations are used first, to limit the search region for the prototype application to bone and second, to provide a training data sample for the development of an automatic bone segmentation method.

多发性骨髓瘤（MM）是一种累及骨骼的骨髓系统性肿瘤疾病。它在骨骼中引起多个肿瘤，但也引起溶骨性病变，导致骨组织的弥漫性或局灶性溶解。这些病变导致疼痛的骨破坏和骨折。目前，无论是整体肿瘤质量还是骨骼变化的程度都无法量化。因此，需要对每一个病灶进行分割。大量的病变和对整个骨骼的检查阻碍了临床医生日常工作中对所有病变的手动分割。肿瘤和溶骨性病变的描述需要多种模式，因为病变在不同模式下表现出不同的特征。只有通过使用自动方法，才能以合理的方式执行此分割任务。目前，没有针对此问题的自动解决方案。了解整体肿瘤体积和骨骼状态的量化可能会导致更准确的分期和治疗监测。这种分析的一个重要步骤是单个骨骼的分割。单个骨骼的分割在其他应用中也很重要。对于MM的新治疗策略的分析，对于骨的照射，例如，受影响的造血骨髓的体积是相关的。因此，骨骼的分割是必要的。此外，单个骨骼的分割与肿瘤学和放射疗法中的其他应用高度相关，因为骨骼为治疗期间的运动分析和运动补偿提供了重要信息。在该项目中，将创建数据基础，用于MM中图像分析的进一步自动化。已开发的基于多模态特征的病变分割的病变分类原型在4例MM患者上进行了示例性测试。该项目的目的是分析和改进这个原型应用程序。应用程序将基于更大的数据样本进行训练和测试。因此，需要创建训练数据，这需要手动分割全身图像中的病变和单个骨骼。病变的手动分割是必不可少的，因为它是模式识别方法的基础，该方法使用多模态特征并学习病变和健康组织的特征，并且以后应该能够在新的全身图像中检测病变。首先使用手动骨分割，以将原型应用的搜索区域限制为骨，其次为自动骨分割方法的开发提供训练数据样本。