Correlation of choroidal thickness with age in healthy subjects: automatic detection and segmentation using a deep learning model.

PROPOSE: The proposed deep learning model with a mask region-based convolutional neural network (Mask R-CNN) can predict choroidal thickness automatically. Changes in choroidal thickness with age can be detected with manual measurements. In this study, we aimed to investigate choroidal thickness in a comprehensive aspect in healthy eyes by utilizing the Mask R-CNN model. METHODS: A total of 68 eyes from 57 participants without significant ocular disease were recruited. The participants were allocated to one of three groups according to their age and underwent spectral domain optical coherence tomography (SD-OCT) or enhanced depth imaging OCT (EDI-OCT) centered on the fovea. Each OCT sequence included 25 slices. Physicians labeled the choroidal contours in all the OCT sequences. We applied the Mask R-CNN model for automatic segmentation. Comparisons of choroidal thicknesses were conducted according to age and prediction accuracy. RESULTS: Older age groups had thinner choroids, according to the automatic segmentation results; the mean choroidal thickness was 253.7 ± 41.9 µm in the youngest group, 206.8 ± 35.4 µm in the middle-aged group, and 152.5 ± 45.7 µm in the oldest group (p < 0.01). Measurements obtained using physician sketches demonstrated similar trends. We observed a significant negative correlation between choroidal thickness and age (p < 0.01). The prediction error was lower and less variable in choroids that were thinner than the cutoff point of 280 µm. CONCLUSION: By observing choroid layer continuously and comprehensively. We found that the mean choroidal thickness decreased with age in healthy subjects. The Mask R-CNN model can accurately predict choroidal thickness, especially choroids thinner than 280 µm. This model can enable exploring larger and more varied choroid datasets comprehensively, automatically, and conveniently.

Multiview Contouring for Breast Tumor on Magnetic Resonance Imaging.

The shape and contour of the lesion are shown to be effective features for physicians to identify breast tumor as benign or malignant. The region of the lesion is usually manually created by the physician according to their clinical experience; therefore, contouring tumors on breast magnetic resonance imaging (MRI) is difficult and time-consuming. For this purpose, an automatic contouring method for breast tumors was developed for less burden in the analysis and to decrease the observed bias to help in making decisions clinically. In this study, a multiview segmentation method for detecting and contouring breast tumors in MRI was represented. The preprocessing of the proposed method reduces any amount of noises but preserves the shape and contrast of the breast tumor. The two-dimensional (2D) level-set segmentation method extracts contours of breast tumors from the transverse, coronal, and sagittal planes. The obtained contours are further utilized to generate appropriate three-dimensional (3D) contours. Twenty breast tumor cases were evaluated and the simulation results show that the proposed contouring method was an efficient method for delineating 3D contours of breast tumors in MRI.

Implant volume estimation in direct-to-implant breast reconstruction after nipple-sparing mastectomy.

BACKGROUND: Nipple-sparing mastectomy (NSM) is an increasingly popular alternative to more traditional mastectomy approaches. However, estimating the implant volume during direct-to-implant (DTI) reconstruction following NSM is difficult for surgeons with little-to-moderate experience. We aimed to provide a fast, easy to use, and accurate method to aid in the estimation of implant size for DTI reconstruction using the specimen weight and breast volume. METHODS: A retrospective analysis was performed using data from 145 NSM patients with specific implant types. Standard two-dimensional digital mammograms were obtained in 118 of the patients. Breast morphological factors (specimen weight, mammographic breast density and volume, and implant size and type) were recorded. Curve-fitting and linear regression models were used to develop formulas predicting the implant volume, and the prediction performance of the obtained formulas was evaluated using the prospective data set. RESULTS: Two formulas to estimate the implant size were obtained, one using the specimen weight and one using the breast volume. The coefficients of correlation (R2) in these formulas were over 0.98 and the root mean squared errors were approximately 13. CONCLUSIONS: These implant volume estimate formulas benefit surgeons by providing a preoperative implant volume assessment in DTI reconstruction using the breast volume and an intraoperative assessment using the specimen weight. The implant size estimation formulas obtained in the present study may be applied in a majority of patients.

Using Flow Characteristics in Three-Dimensional Power Doppler Ultrasound Imaging to Predict Complete Responses in Patients Undergoing Neoadjuvant Chemotherapy.

OBJECTIVES: Strategies are needed for the identification of a poor response to treatment and determination of appropriate chemotherapy strategies for patients in the early stages of neoadjuvant chemotherapy for breast cancer. We hypothesize that power Doppler ultrasound imaging can provide useful information on predicting response to neoadjuvant chemotherapy. METHODS: The solid directional flow of vessels in breast tumors was used as a marker of pathologic complete responses (pCR) in patients undergoing neoadjuvant chemotherapy. Thirty-one breast cancer patients who received neoadjuvant chemotherapy and had tumors of 2 to 5 cm were recruited. Three-dimensional power Doppler ultrasound with high-definition flow imaging technology was used to acquire the indices of tumor blood flow/volume, and the chemotherapy response prediction was established, followed by support vector machine classification. RESULTS: The accuracy of pCR prediction before the first chemotherapy treatment was 83.87% (area under the ROC curve [AUC] = 0.6957). After the second chemotherapy treatment, the accuracy of was 87.9% (AUC = 0.756). Trend analysis showed that good and poor responders exhibited different trends in vascular flow during chemotherapy. CONCLUSIONS: This preliminary study demonstrates the feasibility of using the vascular flow in breast tumors to predict chemotherapeutic efficacy.

Three-dimensional region-based segmentation for breast tumors on sonography.

Because malignant and benign breast tumors show different shapes and sizes on sonography, information about tumor shapes and sizes is important for clinical diagnosis. Since sonograms include noise and tissue texture, accurate clinical diagnosis is highly dependent on clinical experience and expertise. However, manually sketching a 3-dimensional (3D) breast tumor contour is a time-consuming and complicated task. Automatic contouring, which provides a contour similar to that of manual sketching of a breast tumor on sonography, may improve diagnostic accuracy. This study presents an efficient method for automatically detecting 3D contours of breast tumors on 3D sonography. The proposed method applies a voxel nearest neighbor filter, a Wiener filter, and an unsharp filter to enhance contrast and reduce noise. After a 3D region-growing algorithm is used to obtain the contour of the breast tumor, postprocessing of the extracted contour is performed to diminish the shadow region of the tumor. This study evaluated 20 tumor cases comprising 10 benign and 10 malignant cases. The results of computer simulation reveal that the proposed 3D segmentation method provides robust contouring for breast sonograms. This approach consistently obtains contours similar to those obtained by manual contouring of a breast tumor and can reduce the time needed to sketch precise contours.

Speckle reduction imaging of breast ultrasound does not improve the diagnostic performance of morphology-based CAD System.

PURPOSE: Speckle reduction imaging (SRI) is a newly developed technique in ultrasound examination. This study aimed to compare the diagnostic performance of SRI and non-SRI breast ultrasound examinations by using a morphology-based computer-aided diagnostic system. METHODS: One hundred ten patients with pathologically proven breast lesions were enrolled consecutively from April 2008 to October 2008. SRI and non-SRI ultrasound images were both obtained at the same examination for each patient. The regions of interest were manually sketched by an experienced physician without histological information. Nineteen practical morphologic features from the extracted contour were calculated and a support vector machine classifier identified the breast tumor as benign or malignant. Conventional binomial receiver operating characteristics curve analysis was used to represent the diagnostic performance of both SRI and non-SRI. RESULTS: Between SRI and non-SRI methods, there were no significant differences in the area under the receiver operating characteristics curve (Az value: 0.82 versus 0.81), the sensitivity (78.9% versus 84.2%), and the specificity (73.6% versus 70.8%). CONCLUSIONS: Based on the morphology study, the performance of breast ultrasound in characterizing the solid breast mass as benign or malignant was not significantly improved with SRI.

Automatic Segment and Quantify Choroid Layer in Myopic eyes: Deep Learning based Model.

PURPOSE: To report a rapid and accurate method based upon deep learning for automatic segmentation and measurement of the choroidal thickness (CT) in myopic eyes, and to determine the relationship between refractive error (RE) and CT. METHODS: Fifty-four healthy subjects 20-39 years of age were retrospectively reviewed. Data reviewed included age, gender, laterality, visual acuity, RE, and Enhanced Depth Imaging Optical Coherence Tomography (EDI-OCT) images. The choroid layer was labeled by manual and automatic method using EDI-OCT. A Mask Region-convolutional Neural Network (Mask R-CNN) model, using deep Residual Network (ResNet) and Feature Pyramid Networks (FPN) as a backbone network, was trained to automatically outline and quantify the choroid layer. RESULTS: ResNet 50 model was adopted for its 90% accuracy rate and 6.97 s average execution time. CT determined by the manual method had a mean thickness of 258.75 ± 66.11 µm, a positive correlation with RE (r = 0.596, p < .01) and significant association with gender (p = .011) and RE (p < .001) in multivariable linear regression analysis. Meanwhile, CT determined by deep learning presented a mean thickness of 226.39 ± 54.65 µm, a positive correlation with RE (r = 0.546, p < .01) and significant association with gender (p = .043) and RE (p < .001) in multivariable linear regression analysis. Both methods revealed that CT decreased with the increase in myopic RE. CONCLUSIONS: This deep learning method using Mask-RCNN was able to successfully determine the relationship between RE and CT in an accurate and rapid way. It could eliminate the need for manual process, while demonstrating a feasible clinical application.

Application of Artificial Intelligence and Deep Learning for Choroid Segmentation in Myopia.

PURPOSE: To investigate the correlation between choroidal thickness and myopia progression using a deep learning method. METHODS: Two data sets, data set A and data set B, comprising of 123 optical coherence tomography (OCT) volumes, were collected to establish the model and verify its clinical utility. The proposed mask region-based convolutional neural network (R-CNN) model, trained with the pretrained weights from the Common Objects in Context database as well as the manually labeled OCT images from data set A, was used to automatically segment the choroid. To verify its clinical utility, the mask R-CNN model was tested with data set B, and the choroidal thickness estimated by the model was also used to explore its relationship with myopia. RESULTS: Compared with the result of manual segmentation in data set B, the error of the automatic choroidal inner and outer boundary segmentation was 6.72 ± 2.12 and 13.75 ± 7.57 µm, respectively. The mean dice coefficient between the region segmented by automatic and manual methods was 93.87% ± 2.89%. The mean difference in choroidal thickness over the Early Treatment Diabetic Retinopathy Study zone between the two methods was 10.52 µm. Additionally, the choroidal thickness estimated using the proposed model was thinner in high-myopic eyes, and axial length was the most significant predictor. CONCLUSIONS: The mask R-CNN model has excellent performance in choroidal segmentation and quantification. In addition, the choroid of high myopia is significantly thinner than that of nonhigh myopia. TRANSLATIONAL RELEVANCE: This work lays the foundations for mask R-CNN models that could aid in the evaluation of more intricate changes occurring in chorioretinal diseases.

Artificial intelligence-aided diagnosis model for acute respiratory distress syndrome combining clinical data and chest radiographs.

Objective: The aim of this study was to develop an artificial intelligence-based model to detect the presence of acute respiratory distress syndrome (ARDS) using clinical data and chest X-ray (CXR) data. Method: The transfer learning method was used to train a convolutional neural network (CNN) model with an external image dataset to extract the image features. Then, the last layer of the model was fine-tuned to determine the probability of ARDS. The clinical data were trained using three machine learning algorithms-eXtreme Gradient Boosting (XGB), random forest (RF), and logistic regression (LR)-to estimate the probability of ARDS. Finally, ensemble-weighted methods were proposed that combined the image model and the clinical data model to estimate the probability of ARDS. An analysis of the importance of clinical features was performed to explore the most important features in detecting ARDS. A gradient-weighted class activation mapping (Grad-CAM) model was used to explain what our CNN sees and understands when making a decision. Results: The proposed ensemble-weighted methods improved the performances of the ARDS classifiers (XGB + CNN, area under the curve [AUC] = 0.916; RF + CNN, AUC = 0.920; LR + CNN, AUC = 0.920; XGB + RF + LR + CNN, AUC = 0.925). In addition, the ML model using clinical data to present the top 15 important features to identify the risk factors of ARDS. Conclusion: This study developed combined machine learning models with clinical data and CXR images to detect ARDS. According to the results of the Shapley Additive exPlanations values and the Grad-CAM techniques, an explicable ARDS diagnosis model is suitable for a real-life scenario.

Intra-tumor flow index can predict the malignant potential of breast tumor: dependent on age and volume.

The aim of this study was to assess tumor vascularity through three dimensional (3D) power Doppler breast ultrasound (US) and propose a decision model for the classification of benign and malignant breast tumors. Patient recruitment for this study was performed consecutively during a 13-mo period (January 2003 to February 2004). A total of 102 benign and 93 malignant solid breast images were analyzed. Three-dimensional power Doppler US imaging was performed using a Voluson730 ultrasound system equipped with a relative stopping power index (RSP) 6 to 12 transducer. The Virtual Organ Computer-aided Analysis (VOCAL)-imaging program (version 2.1) was used to analyze the stored volume. Histogram indices of the vascularization index (VI), flow index (FI) and vascularization-flow index (VFI) for the intra-tumor and for shells with a thickness of 3 mm surrounding the breast lesion were calculated and showed that for both, malignancy had a higher VI, FI and VFI than benignancy, with statistical significance. Multivariate and stepwise logistic regression revealed the model (including patient age, volume and intra-tumor FI in 3D power Doppler vascularity) to be the best choice for malignant breast tumor characterization. The receiver operating characteristics (ROC) index for the performance of the model was 0.926. Histogram indices for the intra-tumor FI in the 3D power Doppler scan are a good choice of parameter for differentiating between malignant and benign tumors with respect to the power of sensitivity, no matter whether one index is suggested or the patients' age and volume are considered.

Mammographic Density Distribution of Healthy Taiwanese Women and its Naturally Decreasing Trend with Age.

We analysed typical mammographic density (MD) distributions of healthy Taiwanese women to augment existing knowledge, clarify cancer risks, and focus public health efforts. From January 2011 to December 2015, 88,193 digital mammograms were obtained from 69,330 healthy Taiwanese women (average, 1.27 mammograms each). MD measurements included dense volume (DV) and volumetric density percentage (VPD) and were quantified by fully automated volumetric density estimation and Box-Cox normalization. Prediction of the declining MD trend was estimated using curve fitting and a rational model. Normalized DV and VPD Lowess curves demonstrated similar but non-identical distributions. In high-density grade participants, the VPD increased from 12.45% in the 35-39-year group to 13.29% in the 65-69-year group but only from 5.21% to 8.47% in low-density participants. Regarding the decreased cumulative VPD percentage, the mean MD declined from 12.79% to 19.31% in the 45-50-year group versus the 50-55-year group. The large MD decrease in the fifth decade in this present study was similar to previous observations of Western women. Obtaining an MD distribution model with age improves the understanding of breast density trends and age variations and provides a reference for future studies on associations between MD and cancer risk.

Diagnosis of hepatic tumors with texture analysis in nonenhanced computed tomography images.

RATIONALE AND OBJECTIVES: Computed tomography (CT) after iodinated contrast agent injection is highly accurate for diagnosis of hepatic tumors. However, iodinating may have problems of renal toxicity and allergic reaction. We aimed to evaluate the potential role of the computer-aided diagnosis (CAD) with texture analysis in the differential of hepatic tumors on nonenhanced CT. MATERIALS AND METHODS: This study evaluated 164 liver lesions (80 malignant tumors and 84 hemangiomas). The suspicious tumor region in the digitized CT image was manually selected and extracted as a circular subimage. Proposed preprocessing adjustments for subimages were used to equalize the information needed for a differential diagnosis. The autocovariance texture features of subimage were extracted and a support vector machine classifier identified the tumor as benign or malignant. RESULTS: The accuracy of the proposed diagnosis system for classifying malignancies is 81.7%, the sensitivity is 75.0%, the specificity is 88.1%, the positive predictive value is 85.7%, and the negative predictive value is 78.7%. CONCLUSIONS: This system differentiates benign from malignant hepatic tumors with relative high accuracy and is therefore clinically useful to reduce patients needed for iodinated contrast agent injection in CT examination. Because the support vector machine is trainable, it could be further optimized if a larger set of tumor images is to be supplied.

Support vector machines in sonography: application to decision making in the diagnosis of breast cancer.

We evaluated a series of pathologically proven breast tumors using the support vector machine (SVM) in the differential diagnosis of solid breast tumors. This study evaluated two ultrasonic image databases, i.e., DB1 and DB2. The DB1 contained 140 ultrasonic images of solid breast nodules (52 malignant and 88 benign). The DB2 contained 250 ultrasonic images of solid breast nodules (35 malignant and 215 benign). The physician-located regions of interest (ROI) of sonography and textual features were utilized to classify breast tumors. An SVM classifier using interpixel textual features classified the tumor as benign or malignant. The receiver operating characteristic (ROC) area index for the proposed system on the DB1 and the DB2 are 0.9695+/-0.0150 and 0.9552+/-0.0161, respectively. The proposed system differentiates solid breast nodules with a relatively high accuracy and helps inexperienced operators avoid misdiagnosis. The main advantage in the proposed system is that the training procedure of SVM was very fast and stable. The training and diagnosis procedure of the proposed system is almost 700 times faster than that of multilayer perception neural networks (MLPs). With the growth of the database, new ultrasonic images can be collected and used as reference cases while performing diagnoses. This study reduces the training and diagnosis time dramatically.

Effectiveness of evaluating tumor vascularization using 3D power Doppler ultrasound with high-definition flow technology in the prediction of the response to neoadjuvant chemotherapy for T2 breast cancer: a preliminary report.

The aim of this study was to evaluate the effectiveness of advanced ultrasound (US) imaging of vascular flow and morphological features in the prediction of a pathologic complete response (pCR) and a partial response (PR) to neoadjuvant chemotherapy for T2 breast cancer.Twenty-nine consecutive patients with T2 breast cancer treated with six courses of anthracycline-based neoadjuvant chemotherapy were enrolled. Three-dimensional (3D) power Doppler US with high-definition flow (HDF) technology was used to investigate the blood flow in and morphological features of the tumors. Six vascularity quantization features, three morphological features, and two vascular direction features were selected and extracted from the US images. A support vector machine was used to evaluate the changes in vascularity after neoadjuvant chemotherapy, and pCR and PR were predicted on the basis of these changes.The most accurate prediction of pCR was achieved after the first chemotherapy cycle, with an accuracy of 93.1% and a specificity of 85.5%, while that of a PR was achieved after the second cycle, with an accuracy of 79.31% and a specificity of 72.22%.Vascularity data can be useful to predict the effects of neoadjuvant chemotherapy. Determination of changes in vascularity after neoadjuvant chemotherapy using 3D power Doppler US with HDF can generate accurate predictions of the patient response, facilitating early decision-making.

Watershed segmentation for breast tumor in 2-D sonography.

Automatic contouring for breast tumors using medical ultrasound (US) imaging may assist physicians without relevant experience, in making correct diagnoses. This study integrates the advantages of neural network (NN) classification and morphological watershed segmentation to extract precise contours of breast tumors from US images. Textural analysis is employed to yield inputs to the NN to classify ultrasonic images. Autocovariance coefficients specify texture features to classify breasts imaged by US using a self-organizing map (SOM). After the texture features in sonography have been classified, an adaptive preprocessing procedure is selected by SOM output. Finally, watershed transformation automatically determines the contours of the tumor. In this study, the proposed method was trained and tested using images from 60 patients. The results of computer simulations reveal that the proposed method always identified similar contours and regions-of-interest (ROIs) to those obtained by manual contouring (by an experienced physician) of the breast tumor in ultrasonic images. As US imaging becomes more widespread, a functional automatic contouring method is essential and its clinical application is becoming urgent. Such a method provides robust and fast automatic contouring of US images. This study is not to emphasize that the automatic contouring technique is superior to the one undertaken manually. Both automatic and manual contours did not, after all, necessarily result in the same factual pathologic border. In computer-aided diagnosis (CAD) applications, automatic segmentation can save much of the time required to sketch a precise contour, with very high stability.

Diagnosis of breast tumors with sonographic texture analysis using wavelet transform and neural networks.

To increase the ability of ultrasonographic technology for the differential diagnosis of solid breast tumors, we describe a novel computer-aided diagnosis (CADx) system using neural networks for classification of breast tumors. Tumor regions and surrounding tissues are segmented from the physician-located region-of-interest (ROI) images by applying our proposed segmentation algorithm. Cooperating with the segmentation algorithm, three feasible features, including variance contrast, autocorrelation contrast and distribution distortion of wavelet coefficients, were extracted from the ROI images for further classification. A multilayered perceptron (MLP) neural network trained using error back-propagation algorithm with momentum was then used for the differential diagnosis of breast tumors on sonograms. In the experiment, 242 cases (including benign breast tumors from 161 patients and carcinomas from 82 patients) were sampled with k-fold cross-validation (k = 10) to evaluate the performance. The receiver operating characteristic (ROC) area index for the proposed CADx system is 0.9396 +/- 0.0183, the sensitivity is 98.77%, the specificity is 81.37%, the positive predictive value is 72.73% and the negative predictive value is 99.24%. Experimental results showed that our diagnosis model performed very well for breast tumor diagnosis.

Stellate masses and histologic grades in breast cancer.

Breast masses with a radiologic stellate pattern often transform into malignancies, but their tendency to be of low histologic grade yields a better survival rate compared with tumors with other patterns on mammography screening. This study was designed to investigate the correlation of histologic grade with stellate features extracted from the coronal plane of 3-D ultrasound images. A pre-processing method was proposed to facilitate the extraction of stellate features. Extracted features were statistically measured to derive a set of indices that quantitatively represent the stellate pattern. These indices then went through a selection procedure to build proper decision trees. The splitting rules of decision trees indicated that stellate tumors are associated with low grade. A set of indices from the low grade-associated rules has the potential to represent the stellate feature. Further investigation of the hypoechoic region of peripheral tissue is essential to establishment of a complete discriminating model for tumor grades.

Left ventricular myocardium segmentation on arterial phase of multi-detector row computed tomography.

RATIONALE AND OBJECTIVES: Variation of left ventricular myocardial volumes correlates closely with ischemic heart diseases. In clinical practice, because physicians and radiologists rely much on myocardial contour to diagnose many different cardiac diseases, automatic segmentation of left ventricular myocardium and quantifying myocardium characteristics is clinically beneficial. This paper presents a hybrid segmentation method for left ventricular myocardium on arterial phase of multi-detector row computed tomography (MDCT) imaging. MATERIALS AND METHODS: The proposed method utilizes an intensity transformation equation as a preprocessing procedure to enhance contrast and reduce noise in MDCT imaging. By setting the centroid of left ventricle (LV) as an initial seed, the conventional region growing method is employed to identify the endocardial contour of LV cavity for each slice. Then the level-set method (LSM) utilizes the extracted endocardial contour as initial contour to delineate the epicardium of LV. The two extracted contours are integrated to form the region of interest (ROI) of the LV. Finally, the ROIs from all slices are combined to obtain the volume of the whole LV myocardium. RESULTS: Twenty-two healthy patients who had no symptoms of ischemic heart disease are applied to evaluate the performance of the proposed method. Compared with manual contours delineated by two experienced experts, the contouring results from computer simulation reveal that the proposed method always identifies similar contours as that obtained by the manual sketching. CONCLUSION: The proposed method provides a robust and fast automatic contouring for LV myocardium on arterial phase of MDCT. The potential role of this technique may save much of the time required to manually sketch a precise contour with high stability.

Left ventricular myocardium segmentation on delayed phase of multi-detector row computed tomography.

RATIONALE AND OBJECTIVES: Advanced ischemic heart disease is usually accompanied by left ventricular (LV) myocardial volume loss and an abnormal enhancing pattern on delayed phase of multi-detector row computed tomography (MDCT). To assist radiologists and physicians in estimating the LV myocardial volume on delayed phase, this paper proposes an adaptive segmentation method for contouring the myocardial region in the delayed-phase MDCT and for computing the volume. MATERIALS AND METHODS: The proposed method uses an anisotropic diffusion filter as a preprocessing procedure to enhance contrast and reduce specks in MDCT imaging. This work picks the middle of mid-ventricular level image slices as the lead slice. The proposed method develops two contouring modes to sketch the myocardium contour on the lead slice. By establishing the obtained contours as the initial contours, the region-growing method is employed to identify the contour of the myocardial region for each slice. The convex-hull finding algorithm is then used to refine the extracted contour. Finally, the width properties of the myocardial region and the morphological operators are used to obtain the entire LV myocardial volume. RESULTS: Twenty-seven healthy patients who had no symptoms of ischemic heart disease are examined to evaluate the performance of the proposed method. Compared with manual contours delineated by two experienced experts, the contouring results using computer simulation reveal that the proposed method reliably identifies contours similar to those obtained using manual sketching. CONCLUSION: The proposed method provides robust contouring for the LV myocardium on delayed-phase MDCT. The potential role of this technique may substantially reduce the time required to sketch manually a precise contour with high stability.

Vascular morphologic information of three-dimensional power Doppler ultrasound is valuable in the classification of breast lesions.

Doppler ultrasound imaging provides vascular information that could characterize benign and malignant breast masses in many previous publications. In this study, we applied vascular quantification and morphology features derived from three-dimensional power Doppler ultrasound as classifiers based on support vector machine. An Az value under the receiver operating characteristic (ROC) curve was used to measure the significance of each vascularization feature. Sixty solid breast tumors were assessed. According to the Az value for the ROC curve of the selected features, the classification performance of the proposed method was 0.8423, indicating that vascular morphologic information is valuable in the classification of breast lesions.