Design and evaluation of a 3D transrectal ultrasound prostate biopsy system

Biopsy of the prostate using ultrasound guidance is the clinical gold standard for diagnosis of prostate adenocarcinoma. The current prostate biopsy procedure is limited to using 2D transrectal ultrasound (TRUS) images to target biopsy sites and record biopsy core locations for postbiopsy confirmation. Localization of the 2D image in its actual 3D position is ambiguous and limits procedural accuracy and reproducibility. We have developed a 3D TRUS prostate biopsy system that provides 3D intrabiopsy information for needle guidance and biopsy location recording. The system conforms to the workflow and imaging technology of the current biopsy procedure, making it easier for clinical integration. In this paper, we describe the system design and validate the system accuracy by performing mock biopsies on US/CT multimodal patient-specific prostate phantoms. Our biopsy system generated 3D patient-specific models of the prostate with volume errors less than 3.5% and mean boundary errors of less than 1 mm. Using the 3D biopsy system, needles were guided to within 2.3 +/- 1.0 mm of 3D targets and with a high probability of biopsying clinically significant tumors. The positions of the actual biopsy sites were accurately localized to within 1.5 +/- 0.8 mm.     

Can a 12 core prostate biopsy increase the detection rate of prostate cancer versus 6 core?: a prospective randomized study in Korea

Several studies suggest that standard 6 core sextant transrectal ultrasound (TRUS) guided biopsies of the prostate provides insufficient material to adequately detect clinically important prostate cancer, and that a larger biopsy cores may improve the cancer detection rate. We performed a prospective randomized trial by comparing 6 and 12 core prostate biopsies to determine whether doubling the number of cores in a sextant biopsy improves the prostate cancer detection rate. We randomized 240 men with an elevated serum total prostate specific antigen (PSA) level, abnormal digital rectal examination (DRE) and/or TRUS suspicious for prostate cancer into a 6 core biopsy group and 12 core biopsy group from Jan. 2002 to Jan. 2003. We acquired 3 cores from the right and left prostate lobes for the 6 core biopsy group and three additional cores from each side more peripheral than a 6 core for the 12 core biopsy group. The 6 core and 12 core biopsy groups were well matched with no significant differences in age, prostate volume, PSA and PSA density. The overall cancer detection rate by prostate biopsy was 15.8% (38/240) and the cancer detection rate was not significantly different between the 6 core biopsy group (14.4%, 17/118) and 12 core biopsy group (17.2%, 21/122) (p=0.60). Our study demonstrates no statistically significant improvement in prostate cancer detection rate by increasing the number of biopsy cores. In conclusion, we believe that the standard 6 core sextant biopsy of the prostate is as effective at detecting prostate cancer as a 12 core biopsy in Korean men.     

Robot-assisted 3D-TRUS guided prostate brachytherapy: system integration and validation

Current transperineal prostate brachytherapy uses transrectal ultrasound (TRUS) guidance and a template at a fixed position to guide needles along parallel trajectories. However, pubic arch interference (PAI) with the implant path obstructs part of the prostate from being targeted by the brachytherapy needles along parallel trajectories. To solve the PAI problem, some investigators have explored other insertion trajectories than parallel, i.e., oblique. However, parallel trajectory constraints in current brachytherapy procedure do not allow oblique insertion. In this paper, we describe a robot-assisted, three-dimensional (3D) TRUS guided approach to solve this problem. Our prototype consists of a commercial robot, and a 3D TRUS imaging system including an ultrasound machine, image acquisition apparatus and 3D TRUS image reconstruction, and display software. In our approach, we use the robot as a movable needle guide, i.e., the robot positions the needle before insertion, but the physician inserts the needle into the patient's prostate. In a later phase of our work, we will include robot insertion. By unifying the robot, ultrasound transducer, and the 3D TRUS image coordinate systems, the position of the template hole can be accurately related to 3D TRUS image coordinate system, allowing accurate and consistent insertion of the needle via the template hole into the targeted position in the prostate. The unification of the various coordinate systems includes two steps, i.e., 3D image calibration and robot calibration. Our testing of the system showed that the needle placement accuracy of the robot system at the "patient's" skin position was 0.15 mm+/-0.06 mm, and the mean needle angulation error was 0.07 degrees. The fiducial localization error (FLE) in localizing the intersections of the nylon strings for image calibration was 0.13 mm, and the FLE in localizing the divots for robot calibration was 0.37 mm. The fiducial registration error for image calibration was 0.12 mm and 0.52 mm for robot calibration. The target registration error for image calibration was 0.23 mm, and 0.68 mm for robot calibration. Evaluation of the complete system showed that needles can be used to target positions in agar phantoms with a mean error of 0.79 mm+/-0.32 mm.     

Sequential Registration-Based Segmentation of the Prostate Gland in MR Image Volumes

Accurate and fast segmentation and volume estimation of the prostate gland in magnetic resonance (MR) images are necessary steps in the diagnosis, treatment, and monitoring of prostate cancer. This paper presents an algorithm for the prostate gland volume estimation based on the semi-automated segmentation of individual slices in T2-weighted MR image sequences. The proposed sequential registration-based segmentation (SRS) algorithm, which was inspired by the clinical workflow during medical image contouring, relies on inter-slice image registration and user interaction/correction to segment the prostate gland without the use of an anatomical atlas. It automatically generates contours for each slice using a registration algorithm, provided that the user edits and approves the marking in some previous slices. We conducted comprehensive experiments to measure the performance of the proposed algorithm using three registration methods (i.e., rigid, affine, and nonrigid). Five radiation oncologists participated in the study where they contoured the prostate MR (T2-weighted) images of 15 patients both manually and using the SRS algorithm. Compared to the manual segmentation, on average, the SRS algorithm reduced the contouring time by 62 % (a speedup factor of 2.64×) while maintaining the segmentation accuracy at the same level as the intra-user agreement level (i.e., Dice similarity coefficient of 91 versus 90 %). The proposed algorithm exploits the inter-slice similarity of volumetric MR image series to achieve highly accurate results while significantly reducing the contouring time.     

Boundary delineation in transrectal ultrasound image for prostate cancer

This paper presents a new advanced automatic edge delineation model for the detection and diagnosis of prostate cancer on transrectal ultrasound (TRUS) images. The proposed model is to improve prostate boundary detection system by modifying a set of preprocessing algorithms including tree-structured nonlinear filter (TSF), directional wavelet transforms (DWT) and tree-structured wavelet transform (TSWT). The model consists of a preprocessing module and a segmentation module. The preprocessing module is implemented for noise suppression, image smoothing and boundary enhancement. The active contours model is used in the segmentation module for prostate boundary detection in two-dimensional (2D) TRUS images. Experimental results show that the addition of the preprocessing module improves the accuracy and sensitivity of the segmentation module, compared to the implementation of the segmentation module alone. It is believed that the proposed automatic boundary detection module for the TRUS images is a promising approach, which provides an efficient and robust detection and diagnosis strategy and acts as "second opinion" for the physician's interpretation of prostate cancer.     

The Efficacy of Target Biopsy of Suspected Cancer Lesions Detected by Magnetic Resonance Imaging and/or Transrectal Ultrasonography during Initial Prostate Biopsies: Comparison of Outcomes between Two Physicians

Background

The efficacy of adding target prostate biopsy (PBx) of suspected cancer lesions identified on magnetic resonance imaging (MRI) and/or transrectal ultrasonography (TRUS) to initial systematic PBx was evaluated. Moreover, the outcomes were compared between 2 physicians.

Methods

We retrospectively investigated 238 patients who underwent first-time PBx in our hospital. All patients were examined with prostate MRI before PBx. Fourteen systematic biopsies were obtained in all patients. When a suspected lesion was present on MRI and/or TRUS, the lesion was the target of target PBx.

Results

The overall detection rate of prostate cancer (PCa) was 45% (106/238). With target PBx, the PCa detection rate was 32% overall, while that of suspected lesions seen only on MRI was 32%, that of suspected lesions seen only on TRUS was 8% and that of suspected lesions seen on both MRI and TRUS was 52%. The same tendency was shown for each physician. Comparing systematic PBx and target PBx, the overall rate of Gleason score (GS) upgrading with target PBx was 13%. The rate of PCa detected only by systematic PBx was 95%. There was no significant difference between the 2 physicians.

Conclusion

In initial PBx, the addition of target PBx of suspected cancer lesions detected by MRI and/or TRUS to systematic PBx might not be useful to improve the cancer detection rate. However, it may enable more accurate risk classification and detection of minute cancers with a high GS.     

The applicability of simultaneous TRUS-CT imaging for the evaluation of prostate seed implants

To study dose-effect relations of prostate implants with I-125 seeds, accurate knowledge of the dose distribution in the prostate is essential. Commonly, a post-implant computed tomography (CT) scan is used to determine the geometry of the implant and to delineate the contours of the prostate. However, the delineation of the prostate on CT slices is very cumbersome due to poor contrast between the prostate capsule and surrounding tissues. Transrectal Ultrasound (TRUS) on the other hand offers good visualization of the prostate but poor visualization of the implanted seeds. The purpose of this study was to investigate the applicability of combining CT with 3D TRUS by means of image fusion. The advantage of fused TRUS-CT imaging is that both prostate contours and implanted seeds will be well visible. In our clinic, post-implant imaging was realized by simultaneously acquiring a TRUS scan and a CT scan. The TRUS transducer was inserted while the patient was on the CT couch and the CT scan was made directly after the TRUS scan, with the probe still in situ. With the TRUS transducer being visible on both TRUS and CT images, the geometrical relationship between both image sets could be defined by registration on the transducer. Having proven the applicability of simultaneous imaging, the accuracy of this registration method was investigated by additional registration on visible seeds, after preregistration on the transducer. In 4 out of 23 investigated cases an automatic grey value registration on seeds failed for each of the investigated cost functions, and in 2 cases for both cost functions, due to poor visibility of the seeds on the TRUS scan. The average deviations of the seed registration with respect to the transducer registration were negligible. However, in a few individual cases the deviations were significant and probably due to movement of the patient between TRUS and CT scan. In case of a registration on the transducer it is important to avoid patient movement in-between the TRUS and CT scan and to keep the time in-between the scans as short as possible. It can be concluded that fusion of a CT scan and a simultaneously made TRUS scan by means of a three-dimensional (3D) transducer is feasible and accurate when performing a registration on the transducer, if necessary, fine-tuned by a registration on seeds. These fused images are likely to be of great value for post-implant dose distribution evaluations.     

磁共振与经直肠超声认知融合引导的经直肠前列腺靶向穿刺活检614例单中心临床研究

本研究旨在探索磁共振(MRI)-经直肠超声(TRUS)认知融合引导下的,经直肠前列腺靶向穿刺活检与经直肠前列腺系统穿刺活检对前列腺癌的检出率的差异性。回顾性纳入自2016年-2018年于我院行前列腺多参数MRI检查后行前列腺穿刺活检的患者614例。对于根据PI-RADS V2评分≥3分的患者同时行MRI-TRUS认知融合靶向活检和系统活检,对PI-RADS V2评分≤2分的患者只行系统活检,比较靶向活检和系统活检在前列腺癌检出率方面的差异。结果显示认知融合靶向活检检出肿瘤342例(75.7%),系统活检检出肿瘤358例(79.2%),二者对于前列腺癌的检出率无显著差异(χ^2=1.621,P=0.203)。靶向活检穿刺针数显著低于系统穿刺活检针数,减少了(9.3±0.11)针(P <0.001)。靶向穿刺活检阳性针中肿瘤组织平均占比比系统穿刺活检多10.8%(P <0.001)。研究结果表明MRI-TRUS认知融合引导的靶向穿刺与系统穿刺对于前列腺癌都具有较高的检出率。MRI-TRUS有望减少穿刺针数,并能提供更多肿瘤组织。     

Algorithms for radiological image registration and their clinical application

This paper reviews recent work in radiological image registration and provides a classification of image registration by type of transformation and by methods employed to compute the transformation. The former includes transformation of 2D images to 2D images of the same individual, transformation of 3D images to 3D images of the same individual, transformation of images to an atlas or model, transformation of images acquired from a number of individuals, transformations for image guided interventions including 2D to 3D registration and finally tissue deformation in image guided interventions. Recent work on computing transformations for registration using corresponding landmark based registration, surface based registration and voxel similarity measures, including entropy based measures, are reviewed and compared. Recently fully automated algorithms based on voxel similarity measures and, in particular, mutual information have been shown to be accurate and robust at registering images of the head when the rigid body assumption is valid. Two approaches to modelling soft tissue deformation for applications in image guided interventions are described. Validation of complex processing tasks such as image registration is vital if these algorithms are to be used in clinical practice. Three alternative validation strategies are presented. These methods are finding application outside the original domain of radiological imaging.     

基于图谱的肝脏CT三维自动分割研究

目的在肝脏外科手术或肝脏病理研究中,计算肝脏体积是重要步骤。由于肝脏外形复杂、临近组织灰度值与之接近等特点,肝脏的自动医学图像分割仍是医学图像处理中的难点之一。方法本文采用图谱结合3D非刚性配准的方法,同时加入肝脏区域搜索算法,实现了鲁棒性较高的肝脏自动分割程序。首先,利用20套训练图像创建图谱,然后程序自动搜索肝脏区域,最后将图谱与待分割CT图像依次进行仿射配准和B样条配准。配准以后的图谱肝脏轮廓即可表示为目标肝脏分割轮廓,进而计算出肝脏体积。结果评估结果显示,上述方法在肝脏体积误差方面表现出色,达到77分,但在局部（主要在肝脏尖端）出现较大的误差。结论该方法分割临床肝脏CT图像具有可行性。     

Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information

An automatic method for delineating the prostate (including the seminal vesicles) in three-dimensional magnetic resonance scans is presented. The method is based on nonrigid registration of a set of prelabeled atlas images. Each atlas image is nonrigidly registered with the target patient image. Subsequently, the deformed atlas label images are fused to yield a single segmentation of the patient image. The proposed method is evaluated on 50 clinical scans, which were manually segmented by three experts. The Dice similarity coefficient (DSC) is used to quantify the overlap between the automatic and manual segmentations. We investigate the impact of several factors on the performance of the segmentation method. For the registration, two similarity measures are compared: Mutual information and a localized version of mutual information. The latter turns out to be superior (median DeltaDSC approximately equal 0.02, p < 0.01 with a paired two-sided Wilcoxon test) and comes at no added computational cost, thanks to the use of a novel stochastic optimization scheme. For the atlas fusion step we consider a majority voting rule and the "simultaneous truth and performance level estimation" algorithm, both with and without a preceding atlas selection stage. The differences between the various fusion methods appear to be small and mostly not statistically significant (p > 0.05). To assess the influence of the atlas composition, two atlas sets are compared. The first set consists of 38 scans of healthy volunteers. The second set is constructed by a leave-one-out approach using the 50 clinical scans that are used for evaluation. The second atlas set gives substantially better performance (DeltaDSC=0.04, p < 0.01), stressing the importance of a careful atlas definition. With the best settings, a median DSC of around 0.85 is achieved, which is close to the median interobserver DSC of 0.87. The segmentation quality is especially good at the prostate-rectum interface, where the segmentation error remains below 1 mm in 50% of the cases and below 1.5 mm in 75% of the cases.     

MRI/TRUS data fusion for prostate brachytherapy. Preliminary results

Prostate brachytherapy involves implanting radioactive seeds (I125 for instance) permanently in the gland for the treatment of localized prostate cancers, e.g., cT1c-T2a N0 M0 with good prognostic factors. Treatment planning and seed implanting are most often based on the intensive use of transrectal ultrasound (TRUS) imaging. This is not easy because prostate visualization is difficult in this imaging modality particularly as regards the apex of the gland and from an intra- and interobserver variability standpoint. Radioactive seeds are implanted inside open interventional MR machines in some centers. Since MRI was shown to be sensitive and specific for prostate imaging whilst open MR is prohibitive for most centers and makes surgical procedures very complex, this work suggests bringing the MR virtually in the operating room with MRI/TRUS data fusion. This involves providing the physician with bi-modality images (TRUS plus MRI) intended to improve treatment planning from the data registration stage. The paper describes the method developed and implemented in the PROCUR system. Results are reported for a phantom and first series of patients. Phantom experiments helped characterize the accuracy of the process. Patient experiments have shown that using MRI data linked with TRUS data improves TRUS image segmentation especially regarding the apex and base of the prostate. This may significantly modify prostate volume definition and have an impact on treatment planning.     

Adequate histologic sectioning of prostate needle biopsies

No standard method exists for sampling prostate needle biopsies, although most reports claim to embed 3 cores per block and obtain 3 slices from each block. This study was undertaken to determine the extent of histologic sectioning necessary for optimal examination of prostate biopsies. We prospectively compared the impact on cancer yield of submitting 1 biopsy core per cassette (biopsies from January 2010) with 3 cores per cassette (biopsies from August 2010) from a large national reference laboratory. Between 6 and 12 slices were obtained with the former 1-core method, resulting in 3 to 6 slices being placed on each of 2 slides; for the latter 3-core method, a limit of 6 slices was obtained, resulting in 3 slices being place on each of 2 slides. A total of 6708 sets of 12 to 18 core biopsies were studied, including 3509 biopsy sets from the 1-biopsy-core-per-cassette group (January 2010) and 3199 biopsy sets from the 3-biopsy-cores-percassette group (August 2010). The yield of diagnoses was classified as benign, atypical small acinar proliferation, high-grade prostatic intraepithelial neoplasia, and cancer and was similar with the 2 methods: 46.2%, 8.2%, 4.5%, and 41.1% and 46.7%, 6.3%, 4.4%, and 42.6%, respectively (P = .02). Submission of 1 core or 3 cores per cassette had no effect on the yield of atypical small acinar proliferation, prostatic intraepithelial neoplasia, or cancer in prostate needle biopsies. Consequently, we recommend submission of 3 cores per cassette to minimize labor and cost of processing.     

Automated localization of implanted seeds in 3D TRUS images used for prostate brachytherapy

An algorithm has been developed in this paper to localize implanted radioactive seeds in 3D ultrasound images for a dynamic intraoperative brachytherapy procedure. Segmentation of the seeds is difficult, due to their small size in relatively low quality of transrectal ultrasound (TRUS) images. In this paper, intraoperative seed segmentation in 3D TRUS images is achieved by performing a subtraction of the image before the needle has been inserted, and the image after the seeds have been implanted. The seeds are searched in a "local" space determined by the needle position and orientation information, which are obtained from a needle segmentation algorithm. To test this approach, 3D TRUS images of the agar and chicken tissue phantoms were obtained. Within these phantoms, dummy seeds were implanted. The seed locations determined by the seed segmentation algorithm were compared with those obtained from a volumetric cone-beam flat-panel micro-CT scanner and human observers. Evaluation of the algorithm showed that the rms error in determining the seed locations using the seed segmentation algorithm was 0.98 mm in agar phantoms and 1.02 mm in chicken phantoms.     

三维医学图像的交互式图形处理器快速重建

目的:开发一种基于图形处理器(GPU)的医学三维图像交互式重建系统,用于临床辅助诊断、手术计划等领域。方法:在GPU重建算法基础上使用了八叉树空间结构和多边形辅助光线投射方法实现进一步的优化,分别用基础算法和优化后的算法对一组CT图像进行重建,验证优化效果。结果:本研究实现的优化算法在真实医学三维图像重建中得到了高质量的重建结果,并且比原有的基于GPU的重建算法快2～3倍。结论:本研究实现的三维重建系统能有效加快重建速度,实现交互式快速重建。     

Prostate segmentation by feature enhancement using domain knowledge and adaptive region based operations

Estimation of prostate location and volume is essential in determining a dose plan for ultrasound-guided brachytherapy, a common prostate cancer treatment. However, manual segmentation is difficult, time consuming and prone to variability. In this paper, we present a semi-automatic discrete dynamic contour (DDC) model based image segmentation algorithm, which effectively combines a multi-resolution model refinement procedure together with the domain knowledge of the image class. The segmentation begins on a low-resolution image by defining a closed DDC model by the user. This contour model is then deformed progressively towards higher resolution images. We use a combination of a domain knowledge based fuzzy inference system (FIS) and a set of adaptive region based operators to enhance the edges of interest and to govern the model refinement using a DDC model. The automatic vertex relocation process, embedded into the algorithm, relocates deviated contour points back onto the actual prostate boundary, eliminating the need of user interaction after initialization. The accuracy of the prostate boundary produced by the proposed algorithm was evaluated by comparing it with a manually outlined contour by an expert observer. We used this algorithm to segment the prostate boundary in 114 2D transrectal ultrasound (TRUS) images of six patients scheduled for brachytherapy. The mean distance between the contours produced by the proposed algorithm and the manual outlines was 2.70 +/- 0.51 pixels (0.54 +/- 0.10 mm). We also showed that the algorithm is insensitive to variations of the initial model and parameter values, thus increasing the accuracy and reproducibility of the resulting boundaries in the presence of noise and artefacts.     

MRI-targeted prostate biopsy: key considerations for pathologists

We discuss the role of the pathologist for MRI-targeted prostate biopsy with a focus on specimen processing, reporting of pathological findings and quality assurance in establishing a successful MRI-targeted biopsy programme. The authors discuss the current issues relevant to pathologists regarding MRI-targeted prostate biopsy. In addition, a brief review of the recently published literature was performed using an English literature search on PubMed with a focus on original investigations related to MRI-targeted prostate biopsy. Our search terms included the following: ‘prostate cancer’, ‘pathology’, ‘histology’, ‘reporting’, ‘cores’, ‘imaging’, ‘MRI’ and ‘mpMRI’. Prostate multiparametric magnetic resonance imaging (mp-MRI) and MRI-targeted biopsy has been shown to improve the diagnosis of clinically significant prostatic adenocarcinoma and can affect the management of patients with prostate cancer. The current active surveillance guidelines were based on data from TRUS biopsies and not MRI-targeted biopsies. MRI-targeted biopsy acquires multiple cores of tissue from one or more suspicious lesions found on mp-MRI. The way in which multiple targeted core biopsies obtained from a single image-directed region of interest are analysed and reported can potentially alter the Gleason score and tumour burden as reported on biopsy, which could undoubtedly alter patient management. Pathologists play an important role in the reporting of MRI-targeted prostate biopsies. How we report prostate cancer grade and extent on these biopsies can influence patient management. In addition, the pathologist should be involved in the quality assurance for patients undergoing MRI-targeted prostate biopsy.     

Findings in 12-core transrectal ultrasound-guided prostate needle biopsy that predict more advanced cancer at prostatectomy: analysis of 388 biopsy-prostatectomy pairs

We analyzed 5 features on 12-core transrectal ultrasound-guided prostate needle biopsy (TRUS) to predict the extent of cancer at radical prostatectomy (RP). In 388 TRUS-RP pairs, number of positive cores (NPC), percentage of each core involved (%PC), perineural invasion (PNI), Gleason score (GS), distribution of positive cores (DPC), and preoperative prostate-specific antigen (PSA) were correlated with extraprostatic extension (EPE), seminal vesicle invasion (SVI), positive surgical margin (R1), positive lymph nodes (N1), and tumor volume. All features predicted EPE and SVI. NPC, GS, %PC, and PNI strongly predicted R1 status. RP tumor volume was directly proportional to the NPC and %PC. PSA alone and with selected biopsy findings correlated with tumor volume, stage, SVI, and N1 (P < .0001). Contiguous DPC was a significant risk for EPE and SVI (P < .0001) compared with isolated positive cores. Findings at 12-core TRUS along with preoperative PSA reliably predict advanced local disease and have practical value as guides to effective planning for surgical resections.     

An optimised radial basis function algorithm for fast non-rigid registration of medical images

The registration of multi-modal medical image data is important in the fields of image guided surgery and computer aided medical diagnosis. Registration accuracy is of utmost importance in both fields, however in the former, the speed of registration is equally important. In this paper, we present a point-based ‘fast’ non-rigid registration algorithm which exhibits significant speedups as compared to the non-optimised equivalent algorithm. Additionally, we make use of the parallel nature of the graphics processing unit (GPU) of the video adapter card of a standard PC to gain further speedups. The algorithm achieved sub-second performance when tested on the registration of MR with CT image data of size 256³.     

基于ITK多图谱配准的脑部皮质下核团的分割研究

大量研究表明,阿尔茨海默症(AD)的病变与大脑皮质下核团的萎缩息息相关,某些核团的萎缩(如海马)可能成为AD疾病早期诊断的标志,而皮质下核团的分割是研究核团萎缩模式的重要前提。基于AD患者和正常人各30例3DT1W-MR图像,先结合直方图分析和三维形态学分析方法对图像进行脑组织提取,后采用ITK配准算法将10个脑图谱图像经两阶段分别配准到提取脑组织后的图像空间。第一阶段实现基于均方差的仿射配准,第二阶段实现基于互信息的B样条形变配准,两阶段的配准均采用线性插值法和梯度下降的优化搜索方法。最后采用STAPLE融合算法,对配准后得到的10个目标图像进行图像融合,得到最终的分割结果。结果表明:除尾状核外,分割得到的其余6对核团的体积与常用的FSL-FIRST算法的分割结果无统计学差别(P>0.05);AD患者的右侧伏核和双侧海马发生萎缩(P<0.05)。因此,基于ITK配准框架的多图谱配准分割方法能有效分割MR图像上边界不明确的皮质下核团。