Multi-Scale and Shape Constrained Localized Region-Based Active Contour Segmentation of Uterine Fibroid Ultrasound Images in HIFU Therapy

Purpose

To overcome the severe intensity inhomogeneity and blurry boundaries in HIFU (High Intensity Focused Ultrasound) ultrasound images, an accurate and efficient multi-scale and shape constrained localized region-based active contour model (MSLCV), was developed to accurately and efficiently segment the target region in HIFU ultrasound images of uterine fibroids.

Methods

We incorporated a new shape constraint into the localized region-based active contour, which constrained the active contour to obtain the desired, accurate segmentation, avoiding boundary leakage and excessive contraction. Localized region-based active contour modeling is suitable for ultrasound images, but it still cannot acquire satisfactory segmentation for HIFU ultrasound images of uterine fibroids. We improved the localized region-based active contour model by incorporating a shape constraint into region-based level set framework to increase segmentation accuracy. Some improvement measures were proposed to overcome the sensitivity of initialization, and a multi-scale segmentation method was proposed to improve segmentation efficiency. We also designed an adaptive localizing radius size selection function to acquire better segmentation results.

Results

Experimental results demonstrated that the MSLCV model was significantly more accurate and efficient than conventional methods. The MSLCV model has been quantitatively validated via experiments, obtaining an average of 0.94 for the DSC (Dice similarity coefficient) and 25.16 for the MSSD (mean sum of square distance). Moreover, by using the multi-scale segmentation method, the MSLCV model’s average segmentation time was decreased to approximately 1/8 that of the localized region-based active contour model (the LCV model).

Conclusions

An accurate and efficient multi-scale and shape constrained localized region-based active contour model was designed for the semi-automatic segmentation of uterine fibroid ultrasound (UFUS) images in HIFU therapy. Compared with other methods, it provided more accurate and more efficient segmentation results that are very close to those obtained from manual segmentation by a specialist.     

CTANet: Confidence-Based Threshold Adaption Network for Semi-Supervised Segmentation of Uterine Regions from MR Images for HIFU Treatment

ObjectivesThe accurate preoperative segmentation of the uterus and uterine fibroids from magnetic resonance images (MRI) is an essential step for diagnosis and real-time ultrasound guidance during high-intensity focused ultrasound (HIFU) surgery. Conventional supervised methods are effective techniques for image segmentation. Recently, semi-supervised segmentation approaches have been reported in the literature. One popular technique for semi-supervised methods is to use pseudo-labels to artificially annotate unlabeled data. However, many existing pseudo-label generations rely on a fixed threshold used to generate a confidence map, regardless of the proportion of unlabeled and labeled data.Materials and MethodsTo address this issue, we propose a novel semi-supervised framework called Confidence-based Threshold Adaptation Network (CTANet) to improve the quality of pseudo-labels. Specifically, we propose an online pseudo-labels method to automatically adjust the threshold, producing high-confident unlabeled annotations and boosting segmentation accuracy. To further improve the network's generalization to fit the diversity of different patients, we design a novel mixup strategy by regularizing the network on each layer in the decoder part and introducing a consistency regularization loss between the outputs of two sub-networks in CTANet.ResultsWe compare our method with several state-of-the-art semi-supervised segmentation methods on the same uterine fibroids dataset containing 297 patients. The performance is evaluated by the Dice similarity coefficient, the precision, and the recall. The results show that our method outperforms other semi-supervised learning methods. Moreover, for the same training set, our method approaches the segmentation performance of a fully supervised U-Net (100% annotated data) but using 4 times less annotated data (25% annotated data, 75% unannotated data).ConclusionExperimental results are provided to illustrate the effectiveness of the proposed semi-supervised approach. The proposed method can contribute to multi-class segmentation of uterine regions from MRI for HIFU treatment.     

A multi-step directional generalized gradient vector flow snake for target tumor segmentation in US-guided high-intensity focused ultrasound ablation

Getting precise locations of target tumors can help to ensure ablation of cancerous tissues and avoid unwanted destruction of healthy tissues in high-intensity focused ultrasound (HIFU) treatment system. Because of speckle noise and spurious boundaries in ultrasound images, traditional image segmentation methods are not suitable for achieving the precise locations of target tumors in HIFU ablation. In this paper, a multi-step directional generalized gradient vector flow snake model is introduced for target tumor segmentation. In the first step, the traditional generalized gradient vector flow (GGVF) snake is used to obtain an approximate contour of the tumor. According to the approximate contour, a new distance map is generated. Subsequently, a new directional edge map is created by calculating a scalar product of the gradients of the distance map and the initial image. In this process, the gradient directional information and the magnitude information of the distance map are used to attenuate unwanted edges and highlight the real edges in the new directional edge map. Finally, a refined GGVF field is derived from a diffusion operation of the gradient vectors of the directional edge map. The GGVF field is used to refine the tumor's contour, by directing the approximate contour to edges with the desired gradient directionality. Based on the newly developed snake model, the influences of the spurious boundaries and the speckle noise are significantly reduced in the ultrasound image segmentation. Experimental results indicate that this technique is greatly useful for target tumor segmentation in HIFU treatment system     

Walsh Hadamard Transform for Simple Linear Iterative Clustering (SLIC) Superpixel Based Spectral Clustering of Multimodal MRI Brain Tumor Segmentation

The automated brain tumor segmentation methods are challenging due to the diverse nature of tumors. Recently, the graph based spectral clustering method is utilized for brain tumor segmentation to make high-quality segmentation output. In this paper, a new Walsh Hadamard Transform (WHT) texture for superpixel based spectral clustering is proposed for segmentation of a brain tumor from multimodal MRI images. First, the selected kernels of WHT are utilized for creating texture saliency maps and it becomes the input for the Simple Linear Iterative Clustering (SLIC) algorithm, to generate more precise texture based superpixels. Then the texture superpixels become nodes in the graph of spectral clustering for segmenting brain tumors of MRI images. Finally, the original members of superpixels are recovered to represent Complete Tumor (CT), Tumor Core (TC) and Enhancing Tumor (ET) tissues. The observational results are taken out on BRATS 2015 datasets and evaluated using the Dice Score (DS), Hausdorff Distance (HD) and Volumetric Difference (VD) metrics. The proposed method produces competitive results than other existing clustering methods.     

Impact de la méthode de segmentation dans la caractérisation des adénocarcinomes pulmonaires en TEP-TDM au 18FDG

ObjectiveThe treatment of lung adenocarcinomas is conditioned by the presence of certain genetic abnormalities. Certain quantitative parameters obtained from FDG PET-CT, at the voxel scale, provide tumour shape and texture characteristics and might predict their mutational status. Our objective was to determine the impact of the segmentation method in the characterization of lung adenocarcinomas in FDG PET-CT.MethodsForty-nine patients with pulmonary adenocarcinomas were retrospectively included, with their initial FDG PET-CT image. The studied tumours were big, heterogeneous and difficult to segment automatically. The automatic FLAB algorithm was used with and without manual adjustment. The parameters were extracted and compared to the ALK, PDL1, and KRAS status, in order to compare the performances of the two segmentation methods. Their performance was determined by the ROC curve method.ResultsSeveral parameters were significant to predict genetic status (AUC > 0.65). The best performing parameters were different according to the genes studied and according to the resampling methods used. The results were less dependent on resampling in automatic segmentation without manual adjustment. The best performing parameters were volume dependent parameters for segmentation with manual adjustment, and texture parameters for automatic segmentation without adjustment.ConclusionThe study of texture parameters is more efficient in automatic segmentation that is not manually adjusted, and it is advantageous to use a manual adjustment when studying volume-dependent parameters in the case of very heterogeneous tumors.     

Application of a Patient Derived Xenograft Model for Predicative Study of Uterine Fibroid Disease

Human uterine fibroids, benign tumors derived from the smooth muscle layers of the uterus, impose a major health burden to up to 50% of premenopausal women in their daily life. To improve our understanding of this disease, we developed and characterized a patient-derived xenograft model by subcutaneous transplantation of pieces of human uterine fibroid tissue into three different strains of severe combined immunodeficient mice. Engrafted uterine fibroid tissue preserved the classical morphology with interwoven bundles of smooth muscle cells and an abundant deposition of collagenous matrix, similar to uterine fibroids in situ. The grafts expressed both estrogen receptor 1 and progesterone receptor. Additionally, both receptors were up-regulated by estrogen treatment. Growth of the fibroid grafts was dependent on 17β-estradiol and progesterone supplementation at levels similar to women with the disease and was studied for up to 60 days at maximum. Co-treatment with the antiprogestin mifepristone reduced graft growth (four independent donors, p<0.0001 two-sided t-test), as did treatment with the mTOR inhibitor rapamycin (three independent donors, p<0.0001 two-sided t-test). This in vivo animal model preserves the main histological and functional characteristics of human uterine fibroids, is amenable to intervention by pharmacological treatment, and can thus serve as an adequate model for the development of novel therapies.     

Segmentation of Uterus Using Laparoscopic Ultrasound by an Image-Based Active Contour Approach for Guiding Gynecological Diagnosis and Surgery

In laparoscopic gynecologic surgery, ultrasound has been typically implemented to diagnose urological and gynecological conditions. We applied laparoscopic ultrasonography (using Esaote 7.5~10MHz laparoscopic transducer) on the retrospective analyses of 42 women subjects during laparoscopic extirpation and excision of gynecological tumors in our hospital from August 2011 to August 2013. The objective of our research is to develop robust segmentation technique for isolation and identification of the uterus from the ultrasound images, so as to assess, locate and guide in removing the lesions during laparoscopic operations. Our method enables segmentation of the uterus by the active contour algorithm. We evaluated 42 in-vivo laparoscopic images acquired from the 42 patients (age 39.1 ± 7.2 years old) and selected images pertaining to 4 cases of congenital uterine malformations and 2 cases of pelvic adhesions masses. These cases (n = 6) were used for our uterus segmentation experiments. Based on them, the active contour method was compared with the manual segmentation method by a medical expert using linear regression and the Bland-Altman analysis (used to measure the correlation and the agreement). Then, the Dice and Jaccard indices are computed for measuring the similarity of uterus segmented between computational and manual methods. Good correlation was achieved whereby 84%–92% results fall within the 95% confidence interval in the Student t-test) and we demonstrate that the proposed segmentation method of uterus using laparoscopic images is effective.     

Rotationally resliced 3D prostate segmentation of MR images using Bhattacharyya similarity and active band theory

PurposeIn this article, we propose a novel, semi-automatic segmentation method to process 3D MR images of the prostate using the Bhattacharyya coefficient and active band theory with the goal of providing technical support for computer-aided diagnosis and surgery of the prostate.MethodsOur method consecutively segments a stack of rotationally resectioned 2D slices of a prostate MR image by assessing the similarity of the shape and intensity distribution in neighboring slices. 2D segmentation is first performed on an initial slice by manually selecting several points on the prostate boundary, after which the segmentation results are propagated consecutively to neighboring slices. A framework of iterative graph cuts is used to optimize the energy function, which contains a global term for the Bhattacharyya coefficient with the help of an auxiliary function. Our method does not require previously segmented data for training or for building statistical models, and manual intervention can be applied flexibly and intuitively, indicating the potential utility of this method in the clinic.ResultsWe tested our method on 3D T2-weighted MR images from the ISBI dataset and PROMISE12 dataset of 129 patients, and the Dice similarity coefficients were 90.34 ± 2.21% and 89.32 ± 3.08%, respectively. The comparison was performed with several state-of-the-art methods, and the results demonstrate that the proposed method is robust and accurate, achieving similar or higher accuracy than other methods without requiring training.ConclusionThe proposed algorithm for segmenting 3D MR images of the prostate is accurate, robust, and readily applicable to a clinical environment for computer-aided surgery or diagnosis.     

基于动态方向梯度矢量流模型的脑肿瘤图像分割

目的:针对GVF Snake模型算法收敛容易陷入局部极小值及对初始轮廓位置敏感等缺点,提出一种动态方向梯度矢量流模型(DDGVF),使其更适合医学图像的分割。方法:利用主动轮廓模型的提取和跟踪特定区域内目标轮廓的方法,将其应用于医学图像如CT、MRI和超声图像的处理,以获取特定器官及组织的轮廓。结果:动态方向梯度矢量流场(DDGVF)能够较好地提取出脑肿瘤图像。结论:利用该方法能够较好地分割提取出脑肿瘤图像的肿瘤病变区域,为进一步对其纹理和形状等特征进行描述和分析提供了可靠的依据。     

高强度聚焦超声治疗子宫肌瘤300例的临床疗效分析

目的：探讨高强度聚焦超声（high intensity focused ultrasound,HIFU）治疗子宫肌瘤的临床疗效及其安全性。方法：回顾性分析我院2007年1月～2011年1月300例采用高强度聚焦超声治疗的子宫肌瘤患者的临床资料,比较分析治疗前后瘤体大小、病灶内部回声、临床症状改善情况、血流的变化及超声造影图像改变。结果：HIFU治疗后1、3、6及12个月,瘤体体积均较治疗前显著缩小（P〈0.05）,显效率80.33%（241例）,有效率19.33%（58例）,无效率0.33%（1例）,肌瘤所致的临床症状减轻或消失,瘤体内部回声增强,彩色血流均消失,超声造影瘤体内无灌注。治疗期间,患者出现治疗部位疼痛、骶尾部酸胀及肛门坠胀,治疗结束时症状消失;治疗过程中出现腿部疼痛、麻木症状,经过重新定位后症状消失,少数出现阴道少量流血,给予止血药;治疗后出现血尿,经大量喝水排尿后消失。结论：HIFU治疗子宫肌瘤是安全有效的,在妇科领域便于推广。     

Automatic segmentation of cell nuclei in Feulgen-stained histological sections of prostate cancer and quantitative evaluation of segmentation results

Digital image analysis of cell nuclei is useful to obtain quantitative information for the diagnosis and prognosis of cancer. However, the lack of a reliable automatic nuclear segmentation is a limiting factor for high-throughput nuclear image analysis. We have developed a method for automatic segmentation of nuclei in Feulgen-stained histological sections of prostate cancer. A local adaptive thresholding with an object perimeter gradient verification step detected the nuclei and was combined with an active contour model that featured an optimized initialization and worked within a restricted region to improve convergence of the segmentation of each nucleus. The method was tested on 30 randomly selected image frames from three cases, comparing the results from the automatic algorithm to a manual delineation of 924 nuclei. The automatic method segmented a few more nuclei compared to the manual method, and about 73% of the manually segmented nuclei were also segmented by the automatic method. For each nucleus segmented both manually and automatically, the accuracy (i.e., agreement with manual delineation) was estimated. The mean segmentation sensitivity/specificity were 95%/96%. The results from the automatic method were not significantly different from the ground truth provided by manual segmentation. This opens the possibility for large-scale nuclear analysis based on automatic segmentation of nuclei in Feulgen-stained histological sections.     

A Retrospective Analysis of the Impact of Myomectomy on Survival in Uterine Sarcoma

Laparoscopic myomectomy is a minimally invasive, conservative surgical approach commonly used for the treatment of uterine fibroids. However, there is a lack of effective means to distinguish the nature of uterine tumors prior to surgery. The impact of fibroid morcellation during laparoscopic surgery on the dissemination of cancerous uterine fibroids and long-term survival of patients has gained increasing attention. A retrospective cohort study was conducted to analyze the impact of different surgical approaches on recurrence-free survival (RFS) and overall survival (OS) in patients with a postoperative pathological diagnosis of uterine sarcoma at a single medical center. Patients who underwent the first surgery for uterine fibroids (confined to the uterus) and had a postoperative pathological diagnosis of uterine sarcoma were selected in the Chinese PLA General Hospital from January 2005 to January 2014. Based on the use of fibroid morcellation, the subjects were divided into fibroid morcellation (FM) and total hysterectomy (TH, non-morcellation) groups. Follow-up outcomes, including RFS and OS times, were observed. In total, 59 patients were included, with 30 cases in the FM group and 29 cases in the TH group. There were no significant differences in RFS and OS time between the two groups (RFS: P = 0.16, OS: P = 0.09). Multivariate correlation analysis showed that the impact of a higher grade level on RFS and OS was nearly 2-fold the impact of a lower grade level (RFS: P = 0.04, odds ratio (OR) = 1.97; OS: P = 0.03, OR = 2.29). Intraoperative morcellation, postoperative adjuvant therapy, age, tumor size, FIGO stage, and surgical approach were not risk factors affecting RFS and OS. Fibroid morcellation during laparoscopic surgery (including laparoscopic, transvaginal and transabdominal approaches) had no significant impact on RFS and OS time in patients. However, the 5-year RFS and OS rates were both lower in the FM group than in the TH group. Grade level was a significant risk factor for the prognosis of patients with uterine sarcoma.     

Fully automated segmentation and morphometrical analysis of muscle fiber images.

BACKGROUND: Measurement of muscle fiber size and determination of size distribution is important in the assessment of neuromuscular disease. Fiber size estimation by simple inspection is inaccurate and subjective. Manual segmentation and measurement are time-consuming and tedious. We therefore propose an automated image analysis method for objective, reproducible, and time-saving measurement of muscle fibers in routinely hematoxylin-eosin stained cryostat sections. METHODS: The proposed segmentation technique makes use of recent advances in level set based segmentation, where classical edge based active contours are extended by region based cues, such as color and texture. Segmentation and measurement are performed fully automatically. Multiple morphometric parameters, i.e., cross sectional area, lesser diameter, and perimeter are assessed in a single pass. The performance of the computed method was compared to results obtained by manual measurement by experts. RESULTS: The correct classification rate of the computed method was high (98%). Segmentation and measurement results obtained manually or automatically did not reveal any significant differences. CONCLUSIONS: The presented region based active contour approach has been proven to accurately segment and measure muscle fibers. Complete automation minimizes user interaction, thus, batch processing, as well as objective and reproducible muscle fiber morphometry are provided.     

对比超声造影与磁共振在子宫肌瘤的检测和定性中的应用研究

摘要 目的：探讨超声造影（CEUS）之于磁共振（MRI）在子宫肌瘤的检测和定性中的应用价值。方法：选取2016年5月～2022年3月中国人民解放军总医院第一医学中心经病理证实的46例子宫肌瘤患者作为研究对象,所有患者术前均行CEUS和MRI检查,分析并记录子宫肌瘤的数量、大小、位置和有无强化。两种检查方法检测出子宫肌瘤的大小采用t检验进行比较。两种检查方法检测出子宫肌瘤的数量、位置和有无强化采用x²检验进行比较。采用Pearson相关系数分析MRI和CEUS两种检查方法检测出子宫肌瘤的数量、大小、位置和有无强化的相关性。结果：46例患者术后病理总共检测出115个子宫肌瘤。MRI检测出111个（96.52%）,CEUS检测出106个（92.17%）。MRI和CEUS检查测量子宫肌瘤大小的平均值分别为（4.66±1.83）cm、（4.51±1.74）cm。CEUS检测出的子宫肌瘤中有103个的位置无异议,MRI检测出的子宫肌瘤中有107个的位置无异议。在CEUS检查中,有99个子宫肌瘤显示有强化;在MRI检查中,有102个子宫肌瘤显示有强化。两种影像学方法检测出子宫肌瘤的数量、大小、位置和有无强化对比差异均无统计学意义（均P＞0.05）。通过Pearson相关系数分析MRI和CEUS两种影像学检查方法检测出子宫肌瘤数量、大小、位置和有无强化的相关性分别为0.95、0.96、0.94、0.97（均P＜0.001）。结论：CEUS对子宫肌瘤的检测和定性诊断具有与MRI相同的诊断价值,可作为评估子宫肌瘤的一种替代成像方法。     

Deformable registration-based segmentation of the bowel on Megavoltage CT during pelvic radiotherapy

During pelvic radiotherapy bowel loops (BL) are subject to inter-fraction changes. MVCT images have the potential to provide daily bowel segmentation. We assess the feasibility of deformable registration and contour propagation in replacing manual BL segmentation on MVCT.Four observers delineated BL on the planning kVCT and on one therapy MVCT in eight patients. Inter-observer variations in BLs contouring were quantified using DICE index. BLs were then automatically propagated onto MVCT by a commercial software for image deformation and subsequently manually corrected. The agreement between propagated BL/propagated + manually corrected BL vs manual were quantified using the DICE. Contouring times were also compared. The impact on DVH of using the deformable-registration method was assessed. The same procedures were repeated on high-resolution planning-kVCT and therapy-kVCT.MVCTs are adequate to visualize BL (average DICE: 0.815), although worse than kVCT (average DICE:0.889). When comparing propagated vs manual BL, a poor agreement was found (average DICE: 0.564/0.646 for MVCT/KVCT). After manual correction, average DICE indexes increased to 0.810/0.897. The contouring time was reduced to 15 min with the semi-automatic approach from 30 min with manual contouring. DVH parameters of propagated BL were significantly different from manual BL (p < 0.0001); after manual correction, no significant differences were seen.MVCT are suitable for BL visualization. The use of a software to segment BL on MVCT starting from BL-kVCT contours was feasible if followed by manual correction. The method resulted in a substantial reduction of contouring time without detrimental effect on the quality of bowel segmentation and DVH estimates.     

Semiparametric bayes' proportional odds models for current status data with underreporting

Current status data are a type of interval-censored event time data in which all the individuals are either left or right censored. For example, our motivation is drawn from a cross-sectional study, which measured whether or not fibroid onset had occurred by the age of an ultrasound exam for each woman. We propose a semiparametric Bayesian proportional odds model in which the baseline event time distribution is estimated nonparametrically by using adaptive monotone splines in a logistic regression model and the potential risk factors are included in the parametric part of the mean structure. The proposed approach has the advantage of being straightforward to implement using a simple and efficient Gibbs sampler, whereas alternative semiparametric Bayes' event time models encounter problems for current status data. The model is generalized to allow systematic underreporting in a subset of the data, and the methods are applied to an epidemiologic study of uterine fibroids.     

The Immunohistochemical Expression of MCM-3, -5, and -7 Proteins in the Uterine Fibroids

Uterine fibroids are the most common mesenchymal uterine neoplasms; their prevalence is estimated in 40%–60% of women under 35 and in 70%–80% of women over 50 years of age. The current research aims to focus on the etiopathogenesis of uterine fibroids, the factors that affect their growth, and markers with diagnostic and prognostic properties. The MCM (minichromosome maintenance) protein family consists of peptides whose primary function is participation in the molecular mechanism of creating replication forks while regulating DNA synthesis. The aim of this work was to determine the proliferative potential of uterine fibroid cells based on the expression of the Ki-67 antigen and the MCMs—i.e., MCM-3, MCM-5, and MCM-7. In addition, the expression of estrogen (ER) and progesterone (PgR) receptors was evaluated and correlated with the expression of the abovementioned observations. Ultimately, received results were analyzed in terms of clinical and pathological data. Materials and methods: In forty-four cases of uterine fibroids, immunohistochemical reactions were performed. A tissue microarray (TMA) technique was utilized and analyzed cases were assessed in triplicate. Immunohistochemistry was performed using antibodies against Ki-67 antigen, ER, PgR, MCM-3, MCM-5, and MCM-8 on an automated staining platform. Reactions were digitalized by a histologic scanner and quantified utilizing dedicated software for nuclear analysis. Assessment was based on quantification expression of the three histiospots, each representing one case in TMA. Results: In the study group (uterine fibroids), statistically significant stronger expression of all the investigated MCMs was observed, as compared to the control group. In addition, moderate and strong positive correlations were found between all tested proliferative markers. The expression of the MCM-7 protein also correlated positively with ER and PgR. With regard to clinical and pathological data, there was a negative correlation between the expression of MCMs and the number of both pregnancies and births. Significant reductions in MCM-5 and MCM-7 expression were observed in the group of women receiving oral hormonal contraceptives, while smoking women showed an increase in MCM-7, ER, and PgR. Conclusions: Uterine fibroid cells have greater proliferative potential, as evaluated by expression of the Ki-67 antigen and MCMs, than unaltered myometrial cells of the uterine corpus. The expression of MCM-7 was found to have strong or moderate correlations in all assessed relations. In the context of the clinical data, as well evident proliferative potential of MCMs, further studies are strongly recommended.     

Automatic cell segmentation in cyto- and histometry using dominant contour feature points.

Automatic cell segmentation has various application potentials in cytometry and histometry. In this paper, an automatic cluster (touching) cell segmentation approach using the dominant contour feature points has been presented. Dominant feature points are the locations of indentation on the contour of the cluster. First, dominant feature points on the contour of the cluster are detected by distance profile. Next, using shape features of the cells, these feature points are selected for segmentation. We compared the results of the proposed method with manual segmentation and observed that the method has an overall accuracy about to 82%.     

Spinal Cord Segmentation by One Dimensional Normalized Template Matching: A Novel,Quantitative Technique to Analyze Advanced Magnetic Resonance Imaging Data

Spinal cord segmentation is a developing area of research intended to aid the processing and interpretation of advanced magnetic resonance imaging (MRI). For example, high resolution three-dimensional volumes can be segmented to provide a measurement of spinal cord atrophy. Spinal cord segmentation is difficult due to the variety of MRI contrasts and the variation in human anatomy. In this study we propose a new method of spinal cord segmentation based on one-dimensional template matching and provide several metrics that can be used to compare with other segmentation methods. A set of ground-truth data from 10 subjects was manually-segmented by two different raters. These ground truth data formed the basis of the segmentation algorithm. A user was required to manually initialize the spinal cord center-line on new images, taking less than one minute. Template matching was used to segment the new cord and a refined center line was calculated based on multiple centroids within the segmentation. Arc distances down the spinal cord and cross-sectional areas were calculated. Inter-rater validation was performed by comparing two manual raters (n = 10). Semi-automatic validation was performed by comparing the two manual raters to the semi-automatic method (n = 10). Comparing the semi-automatic method to one of the raters yielded a Dice coefficient of 0.91 +/- 0.02 for ten subjects, a mean distance between spinal cord center lines of 0.32 +/- 0.08 mm, and a Hausdorff distance of 1.82 +/- 0.33 mm. The absolute variation in cross-sectional area was comparable for the semi-automatic method versus manual segmentation when compared to inter-rater manual segmentation. The results demonstrate that this novel segmentation method performs as well as a manual rater for most segmentation metrics. It offers a new approach to study spinal cord disease and to quantitatively track changes within the spinal cord in an individual case and across cohorts of subjects.