A preliminary study on computerized lesion localization in MR mammography using 3D nMITR maps, multilayer cellular neural networks, and fuzzy c-partitioning

Cellular neural networks (CNNs) are massively parallel cellular structures with learning abilities. They can be used to realize complex image processing applications efficiently and in almost real time. In this preliminary study, we propose a novel, robust, and fully automated system based on CNNs to facilitate lesion localization in contrast-enhanced MR mammography, a difficult task requiring the processing of a large number of images with attention paid to minute details. The data set consists of 1170 slices containing one precontrast and five postcontrast bilateral axial MR mammograms from 39 patients with 37 malignant and 39 benign mass lesions acquired using a 1.5 Tesla MR scanner with the following parameters: 3D FLASH sequence, TR/TE 9.80/4.76 ms, flip angle 250, slice thickness 2.5 mm, and 0.625 x 0.625 mm2 in-plane resolution. Six hundred slices with 21 benign and 25 malignant lesions of this set are used for training the CNNs; the remaining data are used for test purposes. The breast region of interest is first segmented from precontrast images using four 2D CNNs connected in cascade, specially designed to minimize false detections due to muscles, heart, lungs, and thoracic cavity. To identify deceptively enhancing regions, a 3D nMITR map of the segmented breast is computed and converted into binary form. During this process tissues that have low degrees of enhancements are discarded. To boost lesions, this binary image is processed by a 3D CNN with a control template consisting of three layers of 11 x 11 cells and a fuzzy c-partitioning output function. A set of decision rules extracted empirically from the training data set based on volume and 3D eccentricity features is used to make final decisions and localize lesions. The segmentation algorithm performs well with high average precision, high true positive volume fraction, and low false positive volume fraction with an overall performance of 0.93 +/- 0.05, 0.96 +/- 0.04, and 0.03 +/- 0.05, respectively (training: 0.93 +/- 0.04, 0.94 +/- 0.04, and 0.02 +/- 0.03; test: 0.93 +/- 0.05, 0.97 +/- 0.03, and 0.05 +/- 0.06). The lesion detection performance of the system is quite satisfactory; for the training data set the maximum detection sensitivity is 100% with false-positive detections of 0.28/lesion, 0.09/slice, and 0.65/case; for the test data set the maximum detection sensitivity is 97% with false-positive detections of 0.43/lesion, 0.11/slice, and 0.68/case. On the average, for a detection sensitivity of 99%, the overall performance of the system is 0.34/lesion, 0.10/slice, and 0.67/case. The system introduced does not require prior information concerning breast anatomy; it is robust and exceptionally effective for detecting breast lesions. The use of CNNs, fuzzy c-partitioning, volume, and 3D eccentricity criteria reduces false-positive detections due to artifacts caused by highly enhanced blood vessels, nipples, and normal parenchyma and artifacts from vascularized tissues in the chest wall due to oversegmentation. We hope that this system will facilitate breast examinations, improve the localization of lesions, and reduce unnecessary mastectomies, especially due to missed multicentric lesions and that almost real-time processing speeds achievable by direct hardware implementations will open up new clinical applications, such as making feasible quasi-automated MR-guided biopsies and acquisition of additional postcontrast lesion images to improve morphological characterizations.     

Automatic segmentation of the aortic root in CT angiography of candidate patients for transcatheter aortic valve implantation

Transcatheter aortic valve implantation is a minimal-invasive intervention for implanting prosthetic valves in patients with aortic stenosis. Accurate automated sizing for planning and patient selection is expected to reduce adverse effects such as paravalvular leakage and stroke. Segmentation of the aortic root in CTA is pivotal to enable automated sizing and planning. We present a fully automated segmentation algorithm to extract the aortic root from CTA volumes consisting of a number of steps: first, the volume of interest is automatically detected, and the centerline through the ascending aorta and aortic root centerline are determined. Subsequently, high intensities due to calcifications are masked. Next, the aortic root is represented in cylindrical coordinates. Finally, the aortic root is segmented using 3D normalized cuts. The method was validated against manual delineations by calculating Dice coefficients and average distance error in 20 patients. The method successfully segmented the aortic root in all 20 cases. The mean Dice coefficient was 0.95 ± 0.03, and the mean radial absolute error was 0.74 ± 0.39 mm, where the interobserver Dice coefficient was 0.95 ± 0.03 and the mean error was 0.68 ± 0.34?mm. The proposed algorithm showed accurate results compared to manual segmentations.     

巨噬细胞培养上清液促进大鼠坐骨神经损伤后修复的机制研究

目的 探讨巨噬细胞培养上清液促进大鼠坐骨神经损伤后修复的效果及机制.方法 SD雄性大鼠30只,体重200～250 g,随机分成对照组、观察组和模型组,每组10只,成功制作大鼠坐骨神经损伤离断模型后,对照组在吻合口间隙注射给予等量生理盐水,观察组注射巨噬细胞培养上清液,缝合后饲养12周处死。结果 观察组与对照组大鼠均无死亡。与对照组比,观察组大鼠坐骨神经电生理检测波幅[(0.16±0.04)V比(0.33±0.05)V,t=7.45,P=3.87E-05]和传导速度[(13.22±6.23)m/s比(24.54±6.36)m/s,t=4.02,P=3.01E-3)]显著提高,潜伏期[(0.74±0.06)ms比(0.53±0.04)ms,t=9.21,P=7.07E-06) ]缩短,差异有统计学意义.观察组坐骨神经形态学分析平均髓鞘厚度[(0.48±0.07)μm比(1.27±0.08)μm,t=23.50,P=2.18E-09) ]?神经纤维数量[(0.69±0.08)/HP比(3.22±0.06)/HP,t=80.01,P=3.77E-14) ]和有髓神经纤维平均直径[(1.08±0.39)μm比(3.63±0.42)μm,t=14.07,P=1.97E-07) ]显著增加,差异有统计学意义(P<0.05).观察组雪旺细胞表达的神经生长因子mRNA(NGF mRNA)[(0.13±0.04)比(0.42±0.05),t=14.32,P=1.68E-07) ]和层粘连蛋白mRNA[(0.07±0.03)比(0.38±0.06),t=14.61,P=1.41E-07) ]含量显著升高,差异有统计学意义(P<0.05).结论 巨噬细胞培养上清液可以有效促进大鼠坐骨神经损伤后修复,可能与促进雪旺细胞表达NGF和Laminin有关.     

A discriminative model-constrained EM approach to 3D MRI brain tissue classification and intensity non-uniformity correction

We describe a fully automated method for tissue classification, which is the segmentation into cerebral gray matter (GM), cerebral white matter (WM), and cerebral spinal fluid (CSF), and intensity non-uniformity (INU) correction in brain magnetic resonance imaging (MRI) volumes. It combines supervised MRI modality-specific discriminative modeling and unsupervised statistical expectation maximization (EM) segmentation into an integrated Bayesian framework. While both the parametric observation models and the non-parametrically modeled INUs are estimated via EM during segmentation itself, a Markov random field (MRF) prior model regularizes segmentation and parameter estimation. Firstly, the regularization takes into account knowledge about spatial and appearance-related homogeneity of segments in terms of pairwise clique potentials of adjacent voxels. Secondly and more importantly, patient-specific knowledge about the global spatial distribution of brain tissue is incorporated into the segmentation process via unary clique potentials. They are based on a strong discriminative model provided by a probabilistic boosting tree (PBT) for classifying image voxels. It relies on the surrounding context and alignment-based features derived from a probabilistic anatomical atlas. The context considered is encoded by 3D Haar-like features of reduced INU sensitivity. Alignment is carried out fully automatically by means of an affine registration algorithm minimizing cross-correlation. Both types of features do not immediately use the observed intensities provided by the MRI modality but instead rely on specifically transformed features, which are less sensitive to MRI artifacts. Detailed quantitative evaluations on standard phantom scans and standard real-world data show the accuracy and robustness of the proposed method. They also demonstrate relative superiority in comparison to other state-of-the-art approaches to this kind of computational task: our method achieves average Dice coefficients of 0.93 ± 0.03 (WM) and 0.90 ± 0.05 (GM) on simulated mono-spectral and 0.94 ± 0.02 (WM) and 0.92 ± 0.04 (GM) on simulated multi-spectral data from the BrainWeb repository. The scores are 0.81 ± 0.09 (WM) and 0.82 ± 0.06 (GM) and 0.87 ± 0.05 (WM) and 0.83 ± 0.12 (GM) for the two collections of real-world data sets-consisting of 20 and 18 volumes, respectively-provided by the Internet Brain Segmentation Repository.     

Magnetic resonance angiography reveals increased arterial blood supply and tumorigenesis following high fat feeding in a mouse model of triple‐negative breast cancer

Breast cancer is the second most commonly diagnosed malignancy among women globally. Past MRI studies have linked a high animal fat diet (HAFD) to increased mammary cancer risk in the SV40Tag mouse model of triple‐negative breast cancer. Here, serial MRI examines tumor progression and measures the arterial blood volume feeding mammary glands in low fat diet (LFD) or HAFD fed mice. Virgin female C3(1)SV40Tag mice (n = 8), weaned at 3 weeks old, were assigned to an LFD (n = 4, 3.7 kcal/g, 17.2% kcal from vegetable oil) or an HAFD (n = 4, 5.3 kcal/g, 60% kcal from lard) group. From ages 8 to 12 weeks, weekly fast spin echo MR images and time‐of‐flight (TOF) MR angiography of inguinal mammary glands were acquired at 9.4 T. Following in vivo MRI, mice were sacrificed. Inguinal mammary glands were excised and fixed for ex vivo MRI and histology. Tumor, blood, and mammary gland volumes for each time point were measured from manually traced regions of interest; tumors were classified as invasive by histopathology‐blinded observers. Our analysis confirmed a strong correlation between total tumor volume and blood volume in the mammary gland. Tumor growth rates from weeks 8‐12 were twice as high in HAFD‐fed mice (0.42 ± 0.14/week) as in LFD‐fed mice (0.21 ± 0.03/week), p < 0.004. Mammary gland blood volume growth rate was 2.2 times higher in HAFD mice (0.29 ± 0.11/week) compared with LFD mice (0.13 ± 0.06/week), p < 0.02. The mammary gland growth rate of HAFD‐fed mice (0.071 ± 0.011/week) was 2.7 times larger than that of LFD‐fed mice (0.026 ± 0.009/week), p < 0.01. This is the first non‐invasive, in vivo MRI study to demonstrate a strong correlation between an HAFD and increased cancer burden and blood volume in mammary cancer without using contrast agents, strengthening the evidence supporting the adverse effects of an HAFD on mammary cancer. These results support the potential future use of TOF angiography to evaluate vasculature of suspicious lesions.     

Quantitative Volumetric K-Means Cluster Segmentation of Fibroglandular Tissue and Skin in Breast MRI

Mammographic breast density (MBD) is the most commonly used method to assess the volume of fibroglandular tissue (FGT). However, MRI could provide a clinically feasible and more accurate alternative. There were three aims in this study: (1) to evaluate a clinically feasible method to quantify FGT with MRI, (2) to assess the inter-rater agreement of MRI-based volumetric measurements and (3) to compare them to measurements acquired using digital mammography and 3D tomosynthesis. This retrospective study examined 72 women (mean age 52.4 ± 12.3 years) with 105 disease-free breasts undergoing diagnostic 3.0-T breast MRI and either digital mammography or tomosynthesis. Two observers analyzed MRI images for breast and FGT volumes and FGT-% from T1-weighted images (0.7-, 2.0-, and 4.0-mm-thick slices) using K-means clustering, data from histogram, and active contour algorithms. Reference values were obtained with Quantra software. Inter-rater agreement for MRI measurements made with 2-mm-thick slices was excellent: for FGT-%, r = 0.994 (95% CI 0.990–0.997); for breast volume, r = 0.985 (95% CI 0.934–0.994); and for FGT volume, r = 0.979 (95% CI 0.958–0.989). MRI-based FGT-% correlated strongly with MBD in mammography (r = 0.819–0.904, P < 0.001) and moderately to high with MBD in tomosynthesis (r = 0.630–0.738, P < 0.001). K-means clustering-based assessments of the proportion of the fibroglandular tissue in the breast at MRI are highly reproducible. In the future, quantitative assessment of FGT-% to complement visual estimation of FGT should be performed on a more regular basis as it provides a component which can be incorporated into the individual’s breast cancer risk stratification.     

Characterization of breast tumors using machine learning based upon multiparametric magnetic resonance imaging features

Magnetic resonance imaging (MRI) is playing an important role in the classification of breast tumors. MRI can be used to obtain multiparametric (mp) information, such as structural, hemodynamic, and physiological information. Quantitative analysis of mp-MRI data has shown potential in improving the accuracy of breast tumor classification. In general, a large set of quantitative and texture features can be generated depending upon the type of methodology used. A suitable combination of selected quantitative and texture features can further improve the accuracy of tumor classification. Machine learning (ML) classifiers based upon features derived from MRI data have shown potential in tumor classification. There is a need for further research studies on selecting an appropriate combination of features and evaluating the performance of different ML classifiers for accurate classification of breast tumors. The objective of the current study was to develop and optimize an ML framework based upon mp-MRI features for the characterization of breast tumors (malignant vs. benign and low- vs. high-grade). This study included the breast mp-MRI data of 60 female patients with histopathology results. A total of 128 features were extracted from the mp-MRI tumor data followed by features selection. Five ML classifiers were evaluated for tumor classification using 10-fold crossvalidation with 10 repetitions. The support vector machine (SVM) classifier based on optimum features selected using a wrapper method with an adaptive boosting (AdaBoost) technique provided the highest sensitivity (0.96 ± 0.03), specificity (0.92 ± 0.09), and accuracy (94% ± 2.91%) in the classification of malignant versus benign tumors. This method also provided the highest sensitivity (0.94 ± 0.07), specificity (0.80 ± 0.05), and accuracy (90% ± 5.48%) in the classification of low- versus high-grade tumors. These findings suggest that the SVM classifier outperformed other ML methods in the binary classification of breast tumors.     

容量负荷和压力负荷型心脏肥大大鼠模型的建立及评价

目的： 通过建立压力负荷型和容量负荷型心脏肥大大鼠模型，比较各种评价大鼠心脏结构和功能的方法。 方法： 采用腹主动脉-下腔静脉造瘘法和主动脉缩窄法分别复制容量负荷型和压力负荷型心脏肥大大鼠模型，应用超声心动图、血流动力学检测、心脏称量以及组织切片等多种方法评价心脏结构和功能。 结果： 手术组模型心脏重量均明显大于假手术组。动静脉瘘手术组1周时左心室内径及容积均明显大于假手术组［（0.67±0.03）cm vs （0.60±0.20）cm, （0.69±0.10）mL vs （0.50±0.04）mL，P<0.01］，相对室壁厚度 (RWT) 在手术组明显小于假手术组（0.46±0.05 vs 0.55±0.05，P<0.01）；主动脉缩窄手术组1周时室间隔厚度及左室后壁厚度明显大于假手术组［（0.20±0.03） cm vs （0.16±0.02）cm，P<0.01; （0.20±0.03）cm vs （0.16±0.02）cm，P<0.01］，RWT明显大于假手术组（0.71±0.17 vs （0.56±0.12，P<0.05）， +dp/dtmax明显增加(4 886±1 304 vs 3 674±325，P<0.05)。 2周时上述参数变化更为显著。 结论： 腹主动脉-下腔静脉造瘘和主动脉缩窄方法成功复制了容量负荷型和压力负荷型大鼠心脏肥大模型，通过超声心动图和血流动力学检测可较全面地评价心脏结构和功能变化，其中RWT是两种模型都敏感的指标。     

Transcellular and intercellular transport of anions in the kidney tubules of dogs

Fractional reabsorption of 4 anions was measured in anesthetized dogs either during inhibition of bicarbonate-dependent intercellular NaCl transport by acetazolamide or man-nitol, or during inhibition of transcellular NaCl reabsorption in the diluting segment by ethacrynic acid or ouabain. When administered subsequent to ethacrynic acid, acetazolamide reduced fractional reabsorption of SCN, Br, CI and I by 0.28±0.03, 0.28±0.02, 0.27±0.03 and 0.31±0.03. Mannitol given after ethacrynic acid reduced fractional reabsorptions by 0.23±0.04, 0.20±0.04, 0.20±0.05 and 0.20±0.05, respectively. Thus, the bicarbonate-dependent reabsorption system does not discriminate between these anions. Ethacrynic acid reduced fractional reabsorption of SCN, Br and CI by 0.28±0.05, 0.24±0.03, 0.22±0.03 in one group, by 0.32±0.04, 0.34±0.03, 0.31±0.04 in another group, with significantly smaller reductions for I, 0.07±0.03, in both groups. Ouabain reduced fractional reabsorption of Br, CI and I by 0.48±0.04, 0.46±0.04 and 0.24±0.03, respectively. Thus, anion permeability or transport affinity for bromide, chloride and iodide are equal both for inter- and transcellular transport, while iodide transport is slow along the transcellular route. No specific transport mechanism for chloride was detected.     

Familial Mediterranean fever: High gene frequency among the non-Ashkenazic and ashkenazic Jewish populations in Israel

Familial Mediterranean fever (FMF) is an autosomal recessive recurrent episodic inflammatory disorder that occurs with high frequency in certain populations in the Mediterranean area. Using extended pedigree data of 90 FMF probands, we calculated the FMF gene frequency in various ethnic groups in Israel by analyzing the frequency in a total of 2,312 first cousins. The heterozygote frequencies were as follows: 1:4.9 (0.2 ± 0.06) for the Libyan subgroup, 1:6.4 (0.16 ± 0.03) for the other North African countries subgroup, 1:13.3 (0.07 ± 0.04) for the Iraqi subgroup, 1:11.4 (0.09 ± 0.06) for the Ashkenazic subgroup, and 1:29.4 (0.03 ± 0.03) for the remaining ethnic groups. The observed number of affected parents and offspring of the probands was in agreement with the estimated gene frequency. Thus, the FMF gene frequency is very high in all Jewish ethnic groups in Israel, especially those originating in North African countries. This also explains the parent-to-off-spring transmission of FMF reported in North-African Jews. © 1995 Wiley-Liss, Inc.     

Telomere length and adrenergic-induced left ventricular dilatation and systolic chamber dysfunction in rats

Purpose

The mechanisms responsible for telomere shortening in heart failure are uncertain. We evaluated whether left ventricular (LV) dilatation and systolic chamber dysfunction produced by chronic β-adrenergic receptor (β-AR) activation is associated with leukocyte or cardiac telomere shortening.

Methods

Following 6 months of daily injections of the β-AR agonist, isoproterenol (0.02 mg/kg/day) or the saline vehicle to rats, the extent of LV dilatation and LV systolic chamber dysfunction were determined using echocardiography and isolated perfused heart procedures, and relative telomere length of leukocyte (LTL) and cardiac (CTL) deoxyribonucleic acid were determined using a quantitative real-time polymerase chain reaction assay.

Results

β-AR activation resulted in LV dilatation as indexed by increased LV diastolic diameters (9.2 ± 0.6 vs. 8.4 ± 0.9 mm, P = 0.01) and increased diastolic volume intercepts at zero pressure of the LV diastolic pressure–volume relationship (isolated, perfused heart preparation) (0.40 ± 0.06 vs. 0.37 ± 0.08 ml, P = 0.03). Moreover, β-AR activation resulted in LV systolic chamber dysfunction as indexed by reductions in LV endocardial fractional shortening (0.40 ± 0.05 vs. 0.45 ± 0.06, P = 0.01) and the slope of the LV systolic pressure–volume relation (609 ± 176 vs. 901 ± 230, P = 0.01). Although LTL decreased with age in rats receiving either the β-AR agonist or the saline vehicle (P < 0.05), neither CTL (?0.10 ± 0.14 vs. ?0.15 ± 0.12, P = 0.3) nor LTL (?0.11 ± 0.19 vs. ?0.15 ± 0.18, P = 0.5) were modified by β-AR activation.

Conclusion

In conclusion, chronic β-AR activation sufficient to produce LV dilatation and systolic chamber dysfunction is not associated with alterations in leukocyte or cardiac telomere length. Telomere shortening in chronic heart failure is unlikely to be attributed to chronic β-AR activation.     

Method for Segmentation of Knee Articular Cartilages Based on Contrast-Enhanced CT Images

Segmentation of contrast-enhanced computed tomography (CECT) images enables quantitative evaluation of morphology of articular cartilage as well as the significance of the lesions. Unfortunately, automatic segmentation methods for CECT images are currently lacking. Here, we introduce a semiautomated technique to segment articular cartilage from in vivo CECT images of human knee. The segmented cartilage geometries of nine knee joints, imaged using a clinical CT-scanner with an intra-articular contrast agent, were compared with manual segmentations from CT and magnetic resonance (MR) images. The Dice similarity coefficients (DSCs) between semiautomatic and manual CT segmentations were 0.79–0.83 and sensitivity and specificity values were also high (0.76–0.86). When comparing semiautomatic and manual CT segmentations, mean cartilage thicknesses agreed well (intraclass correlation coefficient?=?0.85–0.93); the difference in thickness (mean?±?SD) was 0.27?±?0.03 mm. Differences in DSC, when MR segmentations were compared with manual and semiautomated CT segmentations, were statistically insignificant. Similarly, differences in volume were not statistically significant between manual and semiautomatic CT segmentations. Semiautomation decreased the segmentation time from 450?±?190 to 42?±?10 min per joint. The results reveal that the proposed technique is fast and reliable for segmentation of cartilage. Importantly, this is the first study presenting semiautomated segmentation of cartilage from CECT images of human knee joint with minimal user interaction.     

Automatic segmentation of left ventricle cavity from short-axis cardiac magnetic resonance images

In this paper, a computational framework is proposed to perform a fully automatic segmentation of the left ventricle (LV) cavity from short-axis cardiac magnetic resonance (CMR) images. In the initial phase, the region of interest (ROI) is automatically identified on the first image frame of the CMR slices. This is done by partitioning the image into different regions using a standard fuzzy c-means (FCM) clustering algorithm where the LV region is identified according to its intensity, size and circularity in the image. Next, LV segmentation is performed within the identified ROI by using a novel clustering method that utilizes an objective functional with a dissimilarity measure that incorporates a circular shape function. This circular shape-constrained FCM algorithm is able to differentiate pixels with similar intensity but are located in different regions (e.g. LV cavity and non-LV cavity), thus improving the accuracy of the segmentation even in the presence of papillary muscles. In the final step, the segmented LV cavity is propagated to the adjacent image frame to act as the ROI. The segmentation and ROI propagation are then iteratively executed until the segmentation has been performed for the whole cardiac sequence. Experiment results using the LV Segmentation Challenge validation datasets show that our proposed framework can achieve an average perpendicular distance (APD) shift of 2.23 ± 0.50 mm and the Dice metric (DM) index of 0.89 ± 0.03, which is comparable to the existing cutting edge methods. The added advantage over state of the art is that our approach is fully automatic, does not need manual initialization and does not require a prior trained model.     

Computer-Aided Segmentation System for Breast MRI Tumour using Modified Automatic Seeded Region Growing (BMRI-MASRG)

In this paper, an automatic computer-aided detection system for breast magnetic resonance imaging (MRI) tumour segmentation will be presented. The study is focused on tumour segmentation using the modified automatic seeded region growing algorithm with a variation of the automated initial seed and threshold selection methodologies. Prior to that, some pre-processing methodologies are involved. Breast skin is detected and deleted using the integration of two algorithms, namely the level set active contour and morphological thinning. The system is applied and tested on 40 test images from the RIDER breast MRI dataset, the results are evaluated and presented in comparison to the ground truths of the dataset. The analysis of variance (ANOVA) test shows that there is a statistically significance in the performance compared to the previous segmentation approaches that have been tested on the same dataset where ANOVA p values for the evaluation measures’ results are less than 0.05, such as: relative overlap (p = 0.0002), misclassification rate (p = 0.045), true negative fraction (p = 0.0001) and sum of true volume fraction (p = 0.0001).     

A fully automated human knee 3D MRI bone segmentation using the ray casting technique

This study aimed at developing a fully automated bone segmentation method for the human knee (femur and tibia) from magnetic resonance (MR) images. MR imaging was acquired on a whole body 1.5T scanner with a gradient echo fat suppressed sequence using an extremity coil. The method was based on the Ray Casting technique which relies on the decomposition of the MR images into multiple surface layers to localize the boundaries of the bones and several partial segmentation objects being automatically merged to obtain the final complete segmentation of the bones. Validation analyses were performed on 161 MR images from knee osteoarthritis patients, comparing the developed fully automated to a validated semi-automated segmentation method, using the average surface distance (ASD), volume correlation coefficient, and Dice similarity coefficient (DSC). For both femur and tibia, respectively, data showed excellent bone surface ASD (0.50 ± 0.12 mm; 0.37 ± 0.09 mm), average oriented distance between bone surfaces within the cartilage domain (0.02 ± 0.07 mm; −0.05 ± 0.10 mm), and bone volume DSC (0.94 ± 0.05; 0.92 ± 0.07). This newly developed fully automated bone segmentation method will enable large scale studies to be conducted within shorter time durations, as well as increase stability in the reading of pathological bone.     

Automatic detection and segmentation of bovine corpora lutea in ultrasonographic ovarian images using genetic programming and rotation invariant local binary patterns

In this study, we propose a fully automatic algorithm to detect and segment corpora lutea (CL) using genetic programming and rotationally invariant local binary patterns. Detection and segmentation experiments were conducted and evaluated on 30 images containing a CL and 30 images with no CL. The detection algorithm correctly determined the presence or absence of a CL in 93.33 % of the images. The segmentation algorithm achieved a mean (±standard deviation) sensitivity and specificity of 0.8693 ± 0.1371 and 0.9136 ± 0.0503, respectively, over the 30 CL images. The mean root mean squared distance of the segmented boundary from the true boundary was 1.12 ± 0.463 mm and the mean maximum deviation (Hausdorff distance) was 3.39 ± 2.00 mm. The success of these algorithms demonstrates that similar algorithms designed for the analysis of in vivo human ovaries are likely viable.     

The Relationship Between Body Mass Index (BMI) and Menstrual Disorders at Different Ages of Menarche and Sex Hormones

Objective

The present research aimed to study the relationship between body mass index and menstrual disorders at different ages of menarche and sex hormones.

风湿性心脏瓣膜病合并房颤中异常钾离子通道的实验研究

目的 探讨心脏瓣膜病合并房颤患者心房组织中异常活动钾离子通道的类型及功能。方法 对2015年8月—2016年7月蚌埠医学院第一附属医院心脏外科32例二尖瓣和/或主动脉瓣置换术患者资料进行回顾性分析。其中,男14例、女18例,年龄48~71岁;房颤患者19例,窦性心律患者13例。在体外循环开始前,常规取房颤患者的左右心房组织及窦性心律患者的右心房组织,置于液氮中备用。通过转录组学及逆转录-PCR方法,对比房颤患者的左右心房组织与窦性心律患者的右心房组织中钾离子通道表达,分析与房颤发生相关的特异性钾离子通道。结果 转录组学分析发现在房颤组中,钾离子通道在生物过程、细胞成分及分子功能上差异均有统计学意义(P值均＜0.01);钾离子通道KCNMB1在房颤组内的左、右房间的表达差异无统计学意义(0.24±0.02 vs 0.28±0.04, P＞0.05),KCNH7 (0.29±0.03 vs 0.45±0.06, P＜0.05)、KCNH2 (0.24±0.04 vs 0.79±0.1, P＜0.01)、KCNJ4 (0.1±0.02 vs 0.75±0.1, P＜0.01)、KCNA6 (0.33±0.03 vs 0.89±0.05, P＜0.01)、KCNK5 (0.21±0.04 vs 0.94±0.04, P＜0.01)和KCNN2 (0.35±0.06 vs 0.58±0.1, P＜0.05)差异均有统计学意义。结论 在心脏瓣膜病合并房颤患者中,多种钾离子通道功能异常,其中KCNH7、KCNH2、KCNJ4、KCNA6、KCNK5及KCNN2与房颤的发展有关。     

Cerebral adrenoleukodystrophy is associated with loss of tolerance to profilin

Childhood cerebral adrenoleukodystrophy (cALD) is a devastating manifestation of ALD accompanied by demyelination, inflammation, and blood brain barrier (BBB) disruption with shared characteristics of an auto‐immune disease. We utilized plasma samples pre‐ and postdevelopment of cALD to determine the presence of specific auto‐antibodies. Mass spectrometry of protein specifically bound with post‐cALD plasma antibody identified Profilin1 (PFN1) as the target. In a screen of 94 boys with cALD 48 (51%) had anti‐PFN1 antibodies, whereas only 2/29 boys with ALD but without cerebral disease, and 0/30 healthy controls showed anti‐PFN1 immunoreactivity. Cerebral spinal fluid from those with cALD showed higher levels of PFN1 protein compared with non‐cALD samples (324 ± 634 versus 42 ± 23 pg/mL, p = 0.04). Boys that were anti‐PFN positive had a significant increase in the amount of gadolinium signal observed on MRI when compared to boys that were anti‐PFN1 negative (p = 0.04) possibly indicating increased BBB disruption. Anti‐PFN1 positivity was also associated with elevated levels of very long chain fatty acids (C26 of 1.12 ± 0.41 versus 0.97 ± 0.30 mg/dL, p = 0.03) and increased plasma BAFF (973 ± 277 versus 733 ± 269 pg/mL, p = 0.03). In conclusion, anti‐PFN may be a novel biomarker associated with the development of cALD in boys with ALD.     

Accuracy Validation of an Automated Method for Prostate Segmentation in Magnetic Resonance Imaging

Three dimensional (3D) manual segmentation of the prostate on magnetic resonance imaging (MRI) is a laborious and time-consuming task that is subject to inter-observer variability. In this study, we developed a fully automatic segmentation algorithm for T2-weighted endorectal prostate MRI and evaluated its accuracy within different regions of interest using a set of complementary error metrics. Our dataset contained 42 T2-weighted endorectal MRI from prostate cancer patients. The prostate was manually segmented by one observer on all of the images and by two other observers on a subset of 10 images. The algorithm first coarsely localizes the prostate in the image using a template matching technique. Then, it defines the prostate surface using learned shape and appearance information from a set of training images. To evaluate the algorithm, we assessed the error metric values in the context of measured inter-observer variability and compared performance to that of our previously published semi-automatic approach. The automatic algorithm needed an average execution time of ～60 s to segment the prostate in 3D. When compared to a single-observer reference standard, the automatic algorithm has an average mean absolute distance of 2.8 mm, Dice similarity coefficient of 82%, recall of 82%, precision of 84%, and volume difference of 0.5 cm³ in the mid-gland. Concordant with other studies, accuracy was highest in the mid-gland and lower in the apex and base. Loss of accuracy with respect to the semi-automatic algorithm was less than the measured inter-observer variability in manual segmentation for the same task.