3D ultrasound image segmentation using wavelet support vector machines

Nomogram Based on CT Radiomics Features Combined With Clinical Factors to Predict Ki-67 Expression in Hepatocellular Carcinoma

Objectives

The study developed and validated a radiomics nomogram based on a combination of computed tomography (CT) radiomics signature and clinical factors and explored the ability of radiomics for individualized prediction of Ki-67 expression in hepatocellular carcinoma (HCC).

Methods

First-order, second-order, and high-order radiomics features were extracted from preoperative enhanced CT images of 172 HCC patients, and the radiomics features with predictive value for high Ki-67 expression were extracted to construct the radiomic signature prediction model. Based on the training group, the radiomics nomogram was constructed based on a combination of radiomic signature and clinical factors that showed an independent association with Ki-67 expression. The area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) were used to verify the performance of the nomogram.

Results

Sixteen higher-order radiomic features that were associated with Ki-67 expression were used to construct the radiomics signature (AUC: training group, 0.854; validation group, 0.744). In multivariate logistic regression, alfa-fetoprotein (AFP) and Edmondson grades were identified as independent predictors of Ki-67 expression. Thus, the radiomics signature was combined with AFP and Edmondson grades to construct the radiomics nomogram (AUC: training group, 0.884; validation group, 0.819). The calibration curve and DCA showed good clinical application of the nomogram.

Conclusion

The radiomics nomogram developed in this study based on the high-order features of CT images can accurately predict high Ki-67 expression and provide individualized guidance for the treatment and clinical monitoring of HCC patients.

Estimation of the Prostate Volume from Abdominal Ultrasound Images by Image-Patch Voting

Estimation of the prostate volume with ultrasound offers many advantages such as portability, low cost, harmlessness, and suitability for real-time operation. Abdominal Ultrasound (AUS) is a practical procedure that deserves more attention in automated prostate-volume-estimation studies. As the experts usually consider automatic end-to-end volume-estimation procedures as non-transparent and uninterpretable systems, we proposed an expert-in-the-loop automatic system that follows the classical prostate-volume-estimation procedures. Our system directly estimates the diameter parameters of the standard ellipsoid formula to produce the prostate volume. To obtain the diameters, our system detects four diameter endpoints from the transverse and two diameter endpoints from the sagittal AUS images as defined by the classical procedure. These endpoints are estimated using a new image-patch voting method to address characteristic problems of AUS images. We formed a novel prostate AUS data set from 305 patients with both transverse and sagittal planes. The data set includes MRI images for 75 of these patients. At least one expert manually marked all the data. Extensive experiments performed on this data set showed that the proposed system results ranged among experts’ volume estimations, and our system can be used in clinical practice.

Bibliometric Analysis of Global Research Trends on Ultrasound Microbubble: A Quickly Developing Field

Background: Microbubbles are widely used as highly effective contrast agents to improve the diagnostic capability of ultrasound imaging. Mounting evidence suggests that ultrasound coupled with microbubbles has promising therapeutic applications in cancer, cardiovascular, and neurological disorders by acting as gene or drug carriers. The aim of this study was to identify the scientific output and activity related to ultrasound microbubble through bibliometric approaches. Methods: The literature related to ultrasound microbubble published between 1998 and 2019 was identified and selected from the Science Citation Index Expanded of Web of Science Core Collection on February 21, 2021. The Scopus database was also searched to validate the results and provided as supplementary material. Quantitative variables including number of publications and citations, H-index, and journal citation reports were analyzed by using Microsoft Excel 2019 and GraphPad Prism 8.0 software. VOS viewer and CiteSpace V were used to perform coauthorship, citation, co-citation, and co-occurrence analysis for countries/regions, institutions, authors, and keywords. Results: A total of 6088 publications from the WoSCC were included. The United States has made the largest contribution in this field, with the majority of publications (2090, 34.3%), citations (90,741, 46.6%), the highest H-index (138), and close collaborations with China and Canada. The most contributive institution was the University of Toronto. Professors De Jong N and Dayton P A have made great achievements in this field. However, the research cooperation between institutions and authors was relatively weak. All the studies could be divided into four clusters: "ultrasound diagnosis study," "microbubbles' characteristics study," "gene therapy study," and "drug delivery study." The average appearing years (AAY) of keywords in the cluster "drug delivery study" was more recent than other clusters. For promising hot spots, "doxorubicin" showed a relatively latest AAY of 2015.49, followed by "nanoparticles" and "breast cancer." Conclusion: There has been an increasing amount of scientific output on ultrasound microbubble according to the global trends, and the United States is staying ahead in this field. Collaboration between research teams still needs to be strengthened. The focus gradually shifts from "ultrasound diagnosis study" to "drug delivery study." It is recommended to pay attention to the latest hot spots, such as "doxorubicin," "nanoparticles," and "breast cancer."

Deep dense multi-path neural network for prostate segmentation in magnetic resonance imaging

PURPOSE

We propose an approach of 3D convolutional neural network to segment the prostate in MR images.

METHODS

A 3D deep dense multi-path convolutional neural network that follows the framework of the encoder-decoder design is proposed. The encoder is built based upon densely connected layers that learn the high-level feature representation of the prostate. The decoder interprets the features and predicts the whole prostate volume by utilizing a residual layout and grouped convolution. A set of sub-volumes of MR images, centered at the prostate, is generated and fed into the proposed network for training purpose. The performance of the proposed network is compared to previously reported approaches.

RESULTS

Two independent datasets were employed to assess the proposed network. In quantitative evaluations, the proposed network achieved 95.11 and 89.01 Dice coefficients for the two datasets. The segmentation results were robust to variations in MR images. In comparison experiments, the segmentation performance of the proposed network was comparable to the previously reported approaches. In qualitative evaluations, the segmentation results by the proposed network were well matched to the ground truth provided by human experts.

CONCLUSIONS

The proposed network is capable of segmenting the prostate in an accurate and robust manner. This approach can be applied to other types of medical images.

A semiautomatic algorithm for three-dimensional segmentation of the prostate on CT images using shape and local texture characteristics

Prostate segmentation in computed tomography (CT) images is useful for planning and guidance of the diagnostic and therapeutic procedures. However, the low soft-tissue contrast of CT images makes the manual prostate segmentation a time-consuming task with high inter-observer variation. We developed a semi-automatic, three-dimensional (3D) prostate segmentation algorithm using shape and texture analysis and have evaluated the method against manual reference segmentations. In a training data set we defined an inter-subject correspondence between surface points in the spherical coordinate system. We applied this correspondence to model the globular and smoothly curved shape of the prostate with 86, well-distributed surface points using a point distribution model that captures prostate shape variation. We also studied the local texture difference between prostate and non-prostate tissues close to the prostate surface. For segmentation, we used the learned shape and texture characteristics of the prostate in CT images and we used a set of user inputs for prostate localization. We trained our algorithm using 23 CT images and tested it on 10 images. We evaluated the results compared with those of two experts' manual reference segmentations using different error metrics. The average measured Dice similarity coefficient (DSC) and mean absolute distance (MAD) were 88 ± 2% and 1.9 ± 0.5 mm, respectively. The averaged inter-expert difference measured on the same dataset was 91 ± 4% (DSC) and 1.3 ± 0.6 mm (MAD). With no prior intra-patient information, the proposed algorithm showed a fast, robust and accurate performance for 3D CT segmentation.

PSNet: prostate segmentation on MRI based on a convolutional neural network

Automatic segmentation of the prostate on magnetic resonance images (MRI) has many applications in prostate cancer diagnosis and therapy. We proposed a deep fully convolutional neural network (CNN) to segment the prostate automatically. Our deep CNN model is trained end-to-end in a single learning stage, which uses prostate MRI and the corresponding ground truths as inputs. The learned CNN model can be used to make an inference for pixel-wise segmentation. Experiments were performed on three data sets, which contain prostate MRI of 140 patients. The proposed CNN model of prostate segmentation (PSNet) obtained a mean Dice similarity coefficient of [Formula: see text] as compared to the manually labeled ground truth. Experimental results show that the proposed model could yield satisfactory segmentation of the prostate on MRI.

A random walk-based segmentation framework for 3D ultrasound images of the prostate

PURPOSE

Accurate segmentation of the prostate on ultrasound images has many applications in prostate cancer diagnosis and therapy. Transrectal ultrasound (TRUS) has been routinely used to guide prostate biopsy. This manuscript proposes a semiautomatic segmentation method for the prostate on three-dimensional (3D) TRUS images.

METHODS

The proposed segmentation method uses a context-classification-based random walk algorithm. Because context information reflects patient-specific characteristics and prostate changes in the adjacent slices, and classification information reflects population-based prior knowledge, we combine the context and classification information at the same time in order to define the applicable population and patient-specific knowledge so as to more accurately determine the seed points for the random walk algorithm. The method is initialized with the user drawing the prostate and non-prostate circles on the mid-gland slice and then automatically segments the prostate on other slices. To achieve reliable classification, we use a new adaptive k-means algorithm to cluster the training data and train multiple decision-tree classifiers. According to the patient-specific characteristics, the most suitable classifier is selected and combined with the context information in order to locate the seed points. By providing accuracy locations of the seed points, the random walk algorithm improves segmentation performance.

RESULTS

We evaluate the proposed segmentation approach on a set of 3D TRUS volumes of prostate patients. The experimental results show that our method achieved a Dice similarity coefficient of 91.0% ± 1.6% as compared to manual segmentation by clinically experienced radiologist.

CONCLUSIONS

The random walk-based segmentation framework, which combines patient-specific characteristics and population information, is effective for segmenting the prostate on ultrasound images. The segmentation method can have various applications in ultrasound-guided prostate procedures.

A supervoxel-based segmentation method for prostate MR images

PURPOSE

Segmentation of the prostate on MR images has many applications in prostate cancer management. In this work, we propose a supervoxel-based segmentation method for prostate MR images.

METHODS

A supervoxel is a set of pixels that have similar intensities, locations, and textures in a 3D image volume. The prostate segmentation problem is considered as assigning a binary label to each supervoxel, which is either the prostate or background. A supervoxel-based energy function with data and smoothness terms is used to model the label. The data term estimates the likelihood of a supervoxel belonging to the prostate by using a supervoxel-based shape feature. The geometric relationship between two neighboring supervoxels is used to build the smoothness term. The 3D graph cut is used to minimize the energy function to get the labels of the supervoxels, which yields the prostate segmentation. A 3D active contour model is then used to get a smooth surface by using the output of the graph cut as an initialization. The performance of the proposed algorithm was evaluated on 30 in-house MR image data and PROMISE12 dataset.

RESULTS

The mean Dice similarity coefficients are 87.2 ± 2.3% and 88.2 ± 2.8% for our 30 in-house MR volumes and the PROMISE12 dataset, respectively. The proposed segmentation method yields a satisfactory result for prostate MR images.

CONCLUSION

The proposed supervoxel-based method can accurately segment prostate MR images and can have a variety of application in prostate cancer diagnosis and therapy.

Deep Learning based Classification for Head and Neck Cancer Detection with Hyperspectral Imaging in an Animal Model

Hyperspectral imaging (HSI) is an emerging imaging modality that can provide a noninvasive tool for cancer detection and image-guided surgery. HSI acquires high-resolution images at hundreds of spectral bands, providing big data to differentiating different types of tissue. We proposed a deep learning based method for the detection of head and neck cancer with hyperspectral images. Since the deep learning algorithm can learn the feature hierarchically, the learned features are more discriminative and concise than the handcrafted features. In this study, we adopt convolutional neural networks (CNN) to learn the deep feature of pixels for classifying each pixel into tumor or normal tissue. We evaluated our proposed classification method on the dataset containing hyperspectral images from 12 tumor-bearing mice. Experimental results show that our method achieved an average accuracy of 91.36%. The preliminary study demonstrated that our deep learning method can be applied to hyperspectral images for detecting head and neck tumors in animal models.

Segmentation of prostate from ultrasound images using level sets on active band and intensity variation across edges

PURPOSE

In this paper, the authors propose a novel efficient method to segment ultrasound images of the prostate with weak boundaries. Segmentation of the prostate from ultrasound images with weak boundaries widely exists in clinical applications. One of the most typical examples is the diagnosis and treatment of prostate cancer. Accurate segmentation of the prostate boundaries from ultrasound images plays an important role in many prostate-related applications such as the accurate placement of the biopsy needles, the assignment of the appropriate therapy in cancer treatment, and the measurement of the prostate volume.

METHODS

Ultrasound images of the prostate are usually corrupted with intensity inhomogeneities, weak boundaries, and unwanted edges, which make the segmentation of the prostate an inherently difficult task. Regarding to these difficulties, the authors introduce an active band term and an edge descriptor term in the modified level set energy functional. The active band term is to deal with intensity inhomogeneities and the edge descriptor term is to capture the weak boundaries or to rule out unwanted boundaries. The level set function of the proposed model is updated in a band region around the zero level set which the authors call it an active band. The active band restricts the authors' method to utilize the local image information in a banded region around the prostate contour. Compared to traditional level set methods, the average intensities inside∖outside the zero level set are only computed in this banded region. Thus, only pixels in the active band have influence on the evolution of the level set. For weak boundaries, they are hard to be distinguished by human eyes, but in local patches in the band region around prostate boundaries, they are easier to be detected. The authors incorporate an edge descriptor to calculate the total intensity variation in a local patch paralleled to the normal direction of the zero level set, which can detect weak boundaries and avoid unwanted edges in the ultrasound images.

RESULTS

The efficiency of the proposed model is demonstrated by experiments on real 3D volume images and 2D ultrasound images and comparisons with other approaches. Validation results on real 3D TRUS prostate images show that the authors' model can obtain a Dice similarity coefficient (DSC) of 94.03% ± 1.50% and a sensitivity of 93.16% ± 2.30%. Experiments on 100 typical 2D ultrasound images show that the authors' method can obtain a sensitivity of 94.87% ± 1.85% and a DSC of 95.82% ± 2.23%. A reproducibility experiment is done to evaluate the robustness of the proposed model.

CONCLUSIONS

As far as the authors know, prostate segmentation from ultrasound images with weak boundaries and unwanted edges is a difficult task. A novel method using level sets with active band and the intensity variation across edges is proposed in this paper. Extensive experimental results demonstrate that the proposed method is more efficient and accurate.

Automatic segmentation of the prostate on CT images using deep learning and multi-atlas fusion

Automatic segmentation of the prostate on CT images has many applications in prostate cancer diagnosis and therapy. However, prostate CT image segmentation is challenging because of the low contrast of soft tissue on CT images. In this paper, we propose an automatic segmentation method by combining a deep learning method and multi-atlas refinement. First, instead of segmenting the whole image, we extract the region of interesting (ROI) to delete irrelevant regions. Then, we use the convolutional neural networks (CNN) to learn the deep features for distinguishing the prostate pixels from the non-prostate pixels in order to obtain the preliminary segmentation results. CNN can automatically learn the deep features adapting to the data, which are different from some handcrafted features. Finally, we select some similar atlases to refine the initial segmentation results. The proposed method has been evaluated on a dataset of 92 prostate CT images. Experimental results show that our method achieved a Dice similarity coefficient of 86.80% as compared to the manual segmentation. The deep learning based method can provide a useful tool for automatic segmentation of the prostate on CT images and thus can have a variety of clinical applications.

Molecular imaging and fusion targeted biopsy of the prostate

PURPOSE

This paper provides a review on molecular imaging with positron emission tomography (PET) and magnetic resonance imaging (MRI) for prostate cancer detection and its applications in fusion targeted biopsy of the prostate.

METHODS

Literature search was performed through the PubMed database using the keywords "prostate cancer", "MRI/ultrasound fusion", "molecular imaging", and "targeted biopsy". Estimates in autopsy studies indicate that 50% of men older than 50 years of age have prostate cancer. Systematic transrectal ultrasound (TRUS) guided prostate biopsy is considered the standard method for prostate cancer detection and has a significant sampling error and a low sensitivity. Molecular imaging technology and new biopsy approaches are emerging to improve the detection of prostate cancer.

RESULTS

Molecular imaging with PET and MRI shows promising results in the early detection of prostate cancer. MRI/TRUS fusion targeted biopsy has become a new clinical standard for the diagnosis of prostate cancer. PET molecular image-directed, three-dimensional ultrasound-guided biopsy is a new technology that has great potential for improving prostate cancer detection rate and for distinguishing aggressive prostate cancer from indolent disease.

CONCLUSION

Molecular imaging and fusion targeted biopsy are active research areas in prostate cancer research.

Computer-aided Detection of Prostate Cancer with MRI: Technology and Applications

One in six men will develop prostate cancer in his lifetime. Early detection and accurate diagnosis of the disease can improve cancer survival and reduce treatment costs. Recently, imaging of prostate cancer has greatly advanced since the introduction of multiparametric magnetic resonance imaging (mp-MRI). Mp-MRI consists of T2-weighted sequences combined with functional sequences including dynamic contrast-enhanced MRI, diffusion-weighted MRI, and magnetic resonance spectroscopy imaging. Because of the big data and variations in imaging sequences, detection can be affected by multiple factors such as observer variability and visibility and complexity of the lesions. To improve quantitative assessment of the disease, various computer-aided detection systems have been designed to help radiologists in their clinical practice. This review paper presents an overview of literatures on computer-aided detection of prostate cancer with mp-MRI, which include the technology and its applications. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application.

Simulating cardiac ultrasound image based on MR diffusion tensor imaging

PURPOSE

Cardiac ultrasound simulation can have important applications in the design of ultrasound systems, understanding the interaction effect between ultrasound and tissue and setting the ground truth for validating quantification methods. Current ultrasound simulation methods fail to simulate the myocardial intensity anisotropies. New simulation methods are needed in order to simulate realistic ultrasound images of the heart.

METHODS

The proposed cardiac ultrasound image simulation method is based on diffusion tensor imaging (DTI) data of the heart. The method utilizes both the cardiac geometry and the fiber orientation information to simulate the anisotropic intensities in B-mode ultrasound images. Before the simulation procedure, the geometry and fiber orientations of the heart are obtained from high-resolution structural MRI and DTI data, respectively. The simulation includes two important steps. First, the backscatter coefficients of the point scatterers inside the myocardium are processed according to the fiber orientations using an anisotropic model. Second, the cardiac ultrasound images are simulated with anisotropic myocardial intensities. The proposed method was also compared with two other nonanisotropic intensity methods using 50 B-mode ultrasound image volumes of five different rat hearts. The simulated images were also compared with the ultrasound images of a diseased rat heart in vivo. A new segmental evaluation method is proposed to validate the simulation results. The average relative errors (AREs) of five parameters, i.e., mean intensity, Rayleigh distribution parameter σ, and first, second, and third quartiles, were utilized as the evaluation metrics. The simulated images were quantitatively compared with real ultrasound images in both ex vivo and in vivo experiments.

RESULTS

The proposed ultrasound image simulation method can realistically simulate cardiac ultrasound images of the heart using high-resolution MR-DTI data. The AREs of their proposed method are 19% for the mean intensity, 17.7% for the scale parameter of Rayleigh distribution, 36.8% for the first quartile of the image intensities, 25.2% for the second quartile, and 19.9% for the third quartile. In contrast, the errors of the other two methods are generally five times more than those of their proposed method.

CONCLUSIONS

The proposed simulation method uses MR-DTI data and realistically generates cardiac ultrasound images with anisotropic intensities inside the myocardium. The ultrasound simulation method could provide a tool for many potential research and clinical applications in cardiac ultrasound imaging.

Combining Population and Patient-Specific Characteristics for Prostate Segmentation on 3D CT Images

Prostate segmentation on CT images is a challenging task. In this paper, we explore the population and patient-specific characteristics for the segmentation of the prostate on CT images. Because population learning does not consider the inter-patient variations and because patient-specific learning may not perform well for different patients, we are combining the population and patient-specific information to improve segmentation performance. Specifically, we train a population model based on the population data and train a patient-specific model based on the manual segmentation on three slice of the new patient. We compute the similarity between the two models to explore the influence of applicable population knowledge on the specific patient. By combining the patient-specific knowledge with the influence, we can capture the population and patient-specific characteristics to calculate the probability of a pixel belonging to the prostate. Finally, we smooth the prostate surface according to the prostate-density value of the pixels in the distance transform image. We conducted the leave-one-out validation experiments on a set of CT volumes from 15 patients. Manual segmentation results from a radiologist serve as the gold standard for the evaluation. Experimental results show that our method achieved an average DSC of 85.1% as compared to the manual segmentation gold standard. This method outperformed the population learning method and the patient-specific learning approach alone. The CT segmentation method can have various applications in prostate cancer diagnosis and therapy.

Random Walk Based Segmentation for the Prostate on 3D Transrectal Ultrasound Images

This paper proposes a new semi-automatic segmentation method for the prostate on 3D transrectal ultrasound images (TRUS) by combining the region and classification information. We use a random walk algorithm to express the region information efficiently and flexibly because it can avoid segmentation leakage and shrinking bias. We further use the decision tree as the classifier to distinguish the prostate from the non-prostate tissue because of its fast speed and superior performance, especially for a binary classification problem. Our segmentation algorithm is initialized with the user roughly marking the prostate and non-prostate points on the mid-gland slice which are fitted into an ellipse for obtaining more points. Based on these fitted seed points, we run the random walk algorithm to segment the prostate on the mid-gland slice. The segmented contour and the information from the decision tree classification are combined to determine the initial seed points for the other slices. The random walk algorithm is then used to segment the prostate on the adjacent slice. We propagate the process until all slices are segmented. The segmentation method was tested in 32 3D transrectal ultrasound images. Manual segmentation by a radiologist serves as the gold standard for the validation. The experimental results show that the proposed method achieved a Dice similarity coefficient of 91.37±0.05%. The segmentation method can be applied to 3D ultrasound-guided prostate biopsy and other applications.

3D in vivo imaging of rat hearts by high frequency ultrasound and its application in myofiber orientation wrapping

Cardiac ultrasound plays an important role in the imaging of hearts in basic cardiovascular research and clinical examinations. 3D ultrasound imaging can provide the geometry or motion information of the heart. Especially, the wrapping of cardiac fiber orientations to the ultrasound volume could supply useful information on the stress distributions and electric action spreading. However, how to acquire 3D ultrasound volumes of the heart of small animals in vivo for cardiac fiber wrapping is still a challenging problem. In this study, we provide an approach to acquire 3D ultrasound volumes of the rat hearts in vivo. The comparison between both in vivo and ex vivo geometries indicated 90.1% Dice similarity. In this preliminary study, the evaluations of the cardiac fiber orientation wrapping errors were 24.7° for the acute angle error and were 22.4° for the inclination angle error. This 3D ultrasound imaging and fiber orientation estimation technique have potential applications in cardiac imaging.

3D prostate TRUS segmentation using globally optimized volume-preserving prior

An efficient and accurate segmentation of 3D transrectal ultrasound (TRUS) images plays an important role in the planning and treatment of the practical 3D TRUS guided prostate biopsy. However, a meaningful segmentation of 3D TRUS images tends to suffer from US speckles, shadowing and missing edges etc, which make it a challenging task to delineate the correct prostate boundaries. In this paper, we propose a novel convex optimization based approach to extracting the prostate surface from the given 3D TRUS image, while preserving a new global volume-size prior. We, especially, study the proposed combinatorial optimization problem by convex relaxation and introduce its dual continuous max-flow formulation with the new bounded flow conservation constraint, which results in an efficient numerical solver implemented on GPUs. Experimental results using 12 patient 3D TRUS images show that the proposed approach while preserving the volume-size prior yielded a mean DSC of 89.5% +/- 2.4%, a MAD of 1.4 +/- 0.6 mm, a MAXD of 5.2 +/- 3.2 mm, and a VD of 7.5% +/- 6.2% in - 1 minute, deomonstrating the advantages of both accuracy and efficiency. In addition, the low standard deviation of the segmentation accuracy shows a good reliability of the proposed approach.

Measuring myofiber orientations from high-frequency ultrasound images using multiscale decompositions

High-frequency ultrasound (HFU) has the ability to image both skeletal and cardiac muscles. The quantitative assessment of these myofiber orientations has a number of applications in both research and clinical examinations; however, difficulties arise due to the severe speckle noise contained in the HFU images. Thus, for the purpose of automatically measuring myofiber orientations from two-dimensional HFU images, we propose a two-step multiscale image decomposition method. It combines a nonlinear anisotropic diffusion filter and a coherence enhancing diffusion filter to extract myofibers. This method has been verified by ultrasound data from simulated phantoms, excised fiber phantoms, specimens of porcine hearts, and human skeletal muscles in vivo. The quantitative evaluations of both phantoms indicated that the myofiber measurements of our proposed method were more accurate than other methods. The myofiber orientations extracted from different layers of the porcine hearts matched the prediction of an established cardiac mode and demonstrated the feasibility of extracting cardiac myofiber orientations from HFU images ex vivo. Moreover, HFU also demonstrated the ability to measure myofiber orientations in vivo.

Prostate segmentation: an efficient convex optimization approach with axial symmetry using 3-D TRUS and MR images

We propose a novel global optimization-based approach to segmentation of 3-D prostate transrectal ultrasound (TRUS) and T2 weighted magnetic resonance (MR) images, enforcing inherent axial symmetry of prostate shapes to simultaneously adjust a series of 2-D slice-wise segmentations in a "global" 3-D sense. We show that the introduced challenging combinatorial optimization problem can be solved globally and exactly by means of convex relaxation. In this regard, we propose a novel coherent continuous max-flow model (CCMFM), which derives a new and efficient duality-based algorithm, leading to a GPU-based implementation to achieve high computational speeds. Experiments with 25 3-D TRUS images and 30 3-D T2w MR images from our dataset, and 50 3-D T2w MR images from a public dataset, demonstrate that the proposed approach can segment a 3-D prostate TRUS/MR image within 5-6 s including 4-5 s for initialization, yielding a mean Dice similarity coefficient of 93.2%±2.0% for 3-D TRUS images and 88.5%±3.5% for 3-D MR images. The proposed method also yields relatively low intra- and inter-observer variability introduced by user manual initialization, suggesting a high reproducibility, independent of observers.

Breast Tissue Classification in Digital Tomosynthesis Images Based on Global Gradient Minimization and Texture Features

Digital breast tomosynthesis (DBT) is a pseudo-three-dimensional x-ray imaging modality proposed to decrease the effect of tissue superposition present in mammography, potentially resulting in an increase in clinical performance for the detection and diagnosis of breast cancer. Tissue classification in DBT images can be useful in risk assessment, computer-aided detection and radiation dosimetry, among other aspects. However, classifying breast tissue in DBT is a challenging problem because DBT images include complicated structures, image noise, and out-of-plane artifacts due to limited angular tomographic sampling. In this project, we propose an automatic method to classify fatty and glandular tissue in DBT images. First, the DBT images are pre-processed to enhance the tissue structures and to decrease image noise and artifacts. Second, a global smooth filter based on L0 gradient minimization is applied to eliminate detailed structures and enhance large-scale ones. Third, the similar structure regions are extracted and labeled by fuzzy C-means (FCM) classification. At the same time, the texture features are also calculated. Finally, each region is classified into different tissue types based on both intensity and texture features. The proposed method is validated using five patient DBT images using manual segmentation as the gold standard. The Dice scores and the confusion matrix are utilized to evaluate the classified results. The evaluation results demonstrated the feasibility of the proposed method for classifying breast glandular and fat tissue on DBT images.

A Minimum Spanning Forest Based Hyperspectral Image Classification Method for Cancerous Tissue Detection

Hyperspectral imaging is a developing modality for cancer detection. The rich information associated with hyperspectral images allow for the examination between cancerous and healthy tissue. This study focuses on a new method that incorporates support vector machines into a minimum spanning forest algorithm for differentiating cancerous tissue from normal tissue. Spectral information was gathered to test the algorithm. Animal experiments were performed and hyperspectral images were acquired from tumor-bearing mice. In vivo imaging experimental results demonstrate the applicability of the proposed classification method for cancer tissue classification on hyperspectral images.

Mapping Cardiac Fiber Orientations from High-Resolution DTI to High-Frequency 3D Ultrasound

The orientation of cardiac fibers affects the anatomical, mechanical, and electrophysiological properties of the heart. Although echocardiography is the most common imaging modality in clinical cardiac examination, it can only provide the cardiac geometry or motion information without cardiac fiber orientations. If the patient's cardiac fiber orientations can be mapped to his/her echocardiography images in clinical examinations, it may provide quantitative measures for diagnosis, personalized modeling, and image-guided cardiac therapies. Therefore, this project addresses the feasibility of mapping personalized cardiac fiber orientations to three-dimensional (3D) ultrasound image volumes. First, the geometry of the heart extracted from the MRI is translated to 3D ultrasound by rigid and deformable registration. Deformation fields between both geometries from MRI and ultrasound are obtained after registration. Three different deformable registration methods were utilized for the MRI-ultrasound registration. Finally, the cardiac fiber orientations imaged by DTI are mapped to ultrasound volumes based on the extracted deformation fields. Moreover, this study also demonstrated the ability to simulate electricity activations during the cardiac resynchronization therapy (CRT) process. The proposed method has been validated in two rat hearts and three canine hearts. After MRI/ultrasound image registration, the Dice similarity scores were more than 90% and the corresponding target errors were less than 0.25 mm. This proposed approach can provide cardiac fiber orientations to ultrasound images and can have a variety of potential applications in cardiac imaging.

Prostate tissue characterization/classification in 144 patient population using wavelet and higher order spectra features from transrectal ultrasound images

In this work, we have proposed an on-line computer-aided diagnostic system called "UroImage" that classifies a Transrectal Ultrasound (TRUS) image into cancerous or non-cancerous with the help of non-linear Higher Order Spectra (HOS) features and Discrete Wavelet Transform (DWT) coefficients. The UroImage system consists of an on-line system where five significant features (one DWT-based feature and four HOS-based features) are extracted from the test image. These on-line features are transformed by the classifier parameters obtained using the training dataset to determine the class. We trained and tested six classifiers. The dataset used for evaluation had 144 TRUS images which were split into training and testing sets. Three-fold and ten-fold cross-validation protocols were adopted for training and estimating the accuracy of the classifiers. The ground truth used for training was obtained using the biopsy results. Among the six classifiers, using 10-fold cross-validation technique, Support Vector Machine and Fuzzy Sugeno classifiers presented the best classification accuracy of 97.9% with equally high values for sensitivity, specificity and positive predictive value. Our proposed automated system, which achieved more than 95% values for all the performance measures, can be an adjunct tool to provide an initial diagnosis for the identification of patients with prostate cancer. The technique, however, is limited by the limitations of 2D ultrasound guided biopsy, and we intend to improve our technique by using 3D TRUS images in the future.

Extracting Cardiac Myofiber Orientations from High Frequency Ultrasound Images

Cardiac myofiber plays an important role in stress mechanism during heart beating periods. The orientation of myofibers decides the effects of the stress distribution and the whole heart deformation. It is important to image and quantitatively extract these orientations for understanding the cardiac physiological and pathological mechanism and for diagnosis of chronic diseases. Ultrasound has been wildly used in cardiac diagnosis because of its ability of performing dynamic and noninvasive imaging and because of its low cost. An extraction method is proposed to automatically detect the cardiac myofiber orientations from high frequency ultrasound images. First, heart walls containing myofibers are imaged by B-mode high frequency (>20 MHz) ultrasound imaging. Second, myofiber orientations are extracted from ultrasound images using the proposed method that combines a nonlinear anisotropic diffusion filter, Canny edge detector, Hough transform, and K-means clustering. This method is validated by the results of ultrasound data from phantoms and pig hearts.

Accuracy Evaluation of a 3D Ultrasound-guided Biopsy System

Early detection of prostate cancer is critical in maximizing the probability of successful treatment. Current systematic biopsy approach takes 12 or more randomly distributed core tissue samples within the prostate and can have a high potential, especially with early disease, for a false negative diagnosis. The purpose of this study is to determine the accuracy of a 3D ultrasound-guided biopsy system. Testing was conducted on prostate phantoms created from an agar mixture which had embedded markers. The phantoms were scanned and the 3D ultrasound system was used to direct the biopsy. Each phantom was analyzed with a CT scan to obtain needle deflection measurements. The deflection experienced throughout the biopsy process was dependent on the depth of the biopsy target. The results for markers at a depth of less than 20 mm, 20-30 mm, and greater than 30 mm were 3.3 mm, 4.7 mm, and 6.2 mm, respectively. This measurement encapsulates the entire biopsy process, from the scanning of the phantom to the firing of the biopsy needle. Increased depth of the biopsy target caused a greater deflection from the intended path in most cases which was due to an angular incidence of the biopsy needle. Although some deflection was present, this system exhibits a clear advantage in the targeted biopsy of prostate cancer and has the potential to reduce the number of false negative biopsies for large lesions.

Automatic Segmentation of Right Ventricle on Ultrasound Images Using Sparse Matrix Transform and Level Set

An automatic framework is proposed to segment right ventricle on ultrasound images. This method can automatically segment both epicardial and endocardial boundaries from a continuous echocardiography series by combining sparse matrix transform (SMT), a training model, and a localized region based level set. First, the sparse matrix transform extracts main motion regions of myocardium as eigenimages by analyzing statistical information of these images. Second, a training model of right ventricle is registered to the extracted eigenimages in order to automatically detect the main location of the right ventricle and the corresponding transform relationship between the training model and the SMT-extracted results in the series. Third, the training model is then adjusted as an adapted initialization for the segmentation of each image in the series. Finally, based on the adapted initializations, a localized region based level set algorithm is applied to segment both epicardial and endocardial boundaries of the right ventricle from the whole series. Experimental results from real subject data validated the performance of the proposed framework in segmenting right ventricle from echocardiography. The mean Dice scores for both epicardial and endocardial boundaries are 89.1%±2.3% and 83.6±7.3%, respectively. The automatic segmentation method based on sparse matrix transform and level set can provide a useful tool for quantitative cardiac imaging.

PET-directed, 3D Ultrasound-guided prostate biopsy

Multimodatity imaging is a promising approach for improving prostate cancer detection and diagnosis. This article describes various concepts in PET-directed, ultrasound-guided biopsies and highlights a new PET/ultrasound fusion targeted biopsy system for prostate cancer detection.