基于监督学习的前列腺MR/TRUS图像分割和配准方法  

Prostate MR/TRUS image segmentation and registration methods based on supervised learning

作　　者：丛明[1] 吴童 刘冬[1] 杨德勇[2] 杜宇 CONG Ming;WU Tong;LIU Dong;YANG De-yong;DU Yu(School of Mechanical Engineering,Dalian University of Technology,Dalian 116024,China;Urology Department,First Affiliated Hospital of Dalian Medical University,Dalian 116024,China;Dalian Dahuazhongtian Technology Co.,Ltd,Dalian 116024,China)

机构地区：[1]大连理工大学机械工程学院,大连116024 [2]大连医科大学附属第一医院泌尿外科,大连116024 [3]大连大华中天科技有限公司,大连116024

出　　处：《工程科学学报》2020年第10期1362-1371,共10页Chinese Journal of Engineering

基　　金：国家自然科学基金资助项目(51575078,51705063)。

摘　　要：前列腺核磁超声图像配准融合有助于实现前列腺肿瘤的靶向穿刺.传统的配准方法主要是针对手动分割的前列腺核磁(Magnetic resonance,MR)和经直肠超声(Trans-rectal ultrasound,TRUS)图像上对应的生理特征点作为参考点,进行刚体或非刚体配准.针对超声图像因成像质量低导致手动分割配准效率低下的问题,提出一种基于监督学习的前列腺MR/TRUS图像自动分割方法,与术前核磁图像进行非刚体配准.首先,针对图像分割任务训练前列腺超声图像的活动表观模型(Active appearance model,AAM),并基于随机森林建立边界驱动的数学模型,实现超声图像自动分割.接着,提取术前分割的核磁图像与自动分割的超声图像建立轮廓的形状特征矢量,进行特征匹配与图像配准.实验结果表明,本文方法能准确实现前列腺超声图像自动分割与配准融合,9组配准结果的戴斯相似性系数(Dice similarity coefficient,DSC)均大于0.98,同时尿道口处特征点的平均定位精度达1.64 mm,相比传统方法具有更高的配准精度.At present,the diagnosis of prostate cancer mainly relies on the level of prostate-specific antigen(PSA)followed by a prostate biopsy.The technology,transrectal ultrasound(TRUS),has been the most popular method for diagnosing prostate cancer because of its advantages,such as real-time,low cost,easy operation.However,the low imaging quality of ultrasound equipment makes it difficult to distinguish regions of malignant tumors from those of healthy tissues from low-quality images,which results in missing diagnoses or overtreating conditions.In contrast,magnetic resonance(MR)images of the prostate can quickly locate the position of malignant tumors.It is crucial to register the annotated MR images and the corresponding TRUS image to perform a targeted biopsy of the prostate tumor.The registration fusion of prostate magnetic resonance and transrectal ultrasound images helps to improve the accuracy of the prostate lesions targeted biopsy.Traditional registration methods that are usually manually selected,specific anatomical landmarks in segmented areas used as a reference,and performed rigid or nonrigid registration,which is inefficient because of the low quality of prostate TRUS images and the substantial differences in pixel intensity of the prostate between MR and TRUS images.This paper proposed a novel prostate MR/TRUS image segmentation and the automatic registration method was based on a supervised learning framework.First,the prostate active appearance model was trained to be applied in the prostate TRUS images segmentation task,and the random forest classifier was used for building a boundary-driven mathematical model to realize automatic segmentation of TRUS images.Then,some sets of MR/TRUS images contour landmarks were computed by matching the corresponding shape descriptors used for registration.The method was validated by comparing the automatic contour segmentation results with standard results,and the registration results with a traditional registration method.Results showed that our method could accurate

关 键 词：前列腺 图像配准 图像分割 随机森林 活动表观模型 

分 类 号：TP391.7[自动化与计算机技术—计算机应用技术]