Computer technology in detection and staging of prostate carcinoma: A review

Exosomes and ultrasound: The future of theranostic applications

Biomaterials and pertaining formulations have been very successful in various diagnostic and therapeutic applications because of its ability to overcome pharmacological limitations. Some of them have gained significant focus in the recent decade for their theranostic properties. Exosomes can be grouped as biomaterials, since they consist of various biological micro/macromolecules and possess all the properties of a stable biomaterial with size in nano range. Significant research has gone into isolation and exploitation of exosomes as potential theranostic agent. However, the limitations in terms of yield, efficacy, and target specificity are continuously being addressed. On the other hand, several nano/microformulations are responsive to physical or chemical alterations and were successfully stimulated by tweaking the physical characteristics of the surrounding environment they are in. Some of them are termed as photodynamic, sonodynamic or thermodynamic therapeutic systems. In this regard, ultrasound and acoustic systems were extensively studied for its ability towards altering the properties of the systems to which they were applied on. In this review, we have detailed about the diagnostic and therapeutic applications of exosomes and ultrasound separately, consisting of their conventional applications, drawbacks, and developments for addressing the challenges. The information were categorized into various sections that provide complete overview of the isolation strategies and theranostic applications of exosomes in various diseases. Then the ultrasound-based disease diagnosis and therapy were elaborated, with special interest towards the use of ultrasound in enhancing the efficacy of nanomedicines and nanodrug delivery systems, Finally, we discussed about the ability of ultrasound in enhancing the diagnostic and therapeutic properties of exosomes, which could be the future of theranostics.

Cluster Analysis of Cell Nuclei in H&E-Stained Histological Sections of Prostate Cancer and Classification Based on Traditional and Modern Artificial Intelligence Techniques

Biomarker identification is very important to differentiate the grade groups in the histopathological sections of prostate cancer (PCa). Assessing the cluster of cell nuclei is essential for pathological investigation. In this study, we present a computer-based method for cluster analyses of cell nuclei and performed traditional (i.e., unsupervised method) and modern (i.e., supervised method) artificial intelligence (AI) techniques for distinguishing the grade groups of PCa. Two datasets on PCa were collected to carry out this research. Histopathology samples were obtained from whole slides stained with hematoxylin and eosin (H&E). In this research, state-of-the-art approaches were proposed for color normalization, cell nuclei segmentation, feature selection, and classification. A traditional minimum spanning tree (MST) algorithm was employed to identify the clusters and better capture the proliferation and community structure of cell nuclei. K-medoids clustering and stacked ensemble machine learning (ML) approaches were used to perform traditional and modern AI-based classification. The binary and multiclass classification was derived to compare the model quality and results between the grades of PCa. Furthermore, a comparative analysis was carried out between traditional and modern AI techniques using different performance metrics (i.e., statistical parameters). Cluster features of the cell nuclei can be useful information for cancer grading. However, further validation of cluster analysis is required to accomplish astounding classification results.

Investigating the Performance of Generative Adversarial Networks for Prostate Tissue Detection and Segmentation

The manual delineation of region of interest (RoI) in 3D magnetic resonance imaging (MRI) of the prostate is time-consuming and subjective. Correct identification of prostate tissue is helpful to define a precise RoI to be used in CAD systems in clinical practice during diagnostic imaging, radiotherapy and monitoring the progress of disease. Conditional GAN (cGAN), cycleGAN and U-Net models and their performances were studied for the detection and segmentation of prostate tissue in 3D multi-parametric MRI scans. These models were trained and evaluated on MRI data from 40 patients with biopsy-proven prostate cancer. Due to the limited amount of available training data, three augmentation schemes were proposed to artificially increase the training samples. These models were tested on a clinical dataset annotated for this study and on a public dataset (PROMISE12). The cGAN model outperformed the U-Net and cycleGAN predictions owing to the inclusion of paired image supervision. Based on our quantitative results, cGAN gained a Dice score of 0.78 and 0.75 on the private and the PROMISE12 public datasets, respectively.

Morphometric study of adipocytes on breast cancer by means of photonic microscopy and image analysis

Worldwide, breast cancer (BrCa) is currently the leading cause of deaths associated to malignant lesions in adult women. Given that some studies have mentioned that peritumoral adipocytes may contribute to breast carcinogenesis, present work sought to quantitative evaluate the morphometry of these cells in a group of adult women. Three thousand six hundred sixty four breast adipocytes, that came from biopsies of a group of adult females with different types of breast carcinomas (ductal, lobular, and mixed) and one with normal tissues, were evaluated through an image analysis (IA) process regarding six morphometric descriptors: area (A), perimeter (P), Feret diameter (F_D ), aspect ratio (AR), roundness factor (RF), and fractal dimension of cellular contour (FD_C ). Data showed that the adipocytes of the normal tissues group were bigger (A: 3398 ± 2331 µm² , P: 239 ± 83 µm, and F_D : 79.9 ± 24.5 µm) than those from BrCa samples (A: 2860 ± 1933 µm² , P: 214 ± 66 µm, and F_D : 73.2 ± 22.5 µm), and presented a more irregular contour (FD_C of 1.370 ± 0.037 for normal group and of 1.335 ± 0.049 for the oncologic one). Moreover, it could be accounted that adipocytes of mixed carcinomas were largest (F_D : 75.1 ± 22.4 µm) than those of lobular lesions (F_D : 61.6 ± 22.6 µm), while the adipocytes of ductal carcinomas were the most oval (AR: 1.421 ± 0.524) and roughest (FD_C : 1.324 ± 0.050) cells. IA results suggest that BrCa lesions can be categorized through a quantitative morphometric evaluation of peritumoral adipocytes. These findings could let the development of an analytical tool to help the Pathologist to enhance the accuracy of the oncologic diagnose.

Ultrasound imaging and segmentation of bone surfaces: A review

Due to its real-time, non-radiation based three-dimensional (3D) imaging capabilities, ultrasound (US) has been incorporated into various orthopedic procedures. However, imaging artifacts, low signal-to-noise ratio (SNR) and bone boundaries appearing several mm in thickness make the analysis of US data difficult. This paper provides a review about the state-of-the-art bone segmentation and enhancement methods developed for two-dimensional (2D) and 3D US data. First, an overview for the appearance of bone surface response in B-mode data is presented. Then, classification of the proposed techniques in terms of the image information being used is provided. Specifically, the focus is given on segmentation and enhancement of B-mode US data. The review is concluded by discussing future directions of research and additional challenges which need to be overcome in order to make this imaging modality more successful in orthopedics.

A comprehensive non-invasive framework for diagnosing prostate cancer

Early detection of prostate cancer increases chances of patients' survival. Our automated non-invasive system for computer-aided diagnosis (CAD) of prostate cancer segments the prostate on diffusion-weighted magnetic resonance images (DW-MRI) acquired at different b-values, estimates its apparent diffusion coefficients (ADC), and classifies their descriptors - empirical cumulative distribution functions (CDF) - with a trained deep learning network. To segment the prostate, an evolving geometric (level-set-based) deformable model is guided by a speed function depending on intensity attributes extracted from the DW-MRI with nonnegative matrix factorization (NMF). For a more robust evolution, the attributes are fused with a probabilistic shape prior and estimated spatial dependencies between prostate voxels. To preserve continuity, the ADCs of the segmented prostate volume at different b-values are normalized and refined using a generalized Gauss-Markov random field image model. The CDFs of the refined ADCs at different b-values are considered global water diffusion features and used to distinguish between benign and malignant prostates. A deep learning network of stacked non-negativity-constrained auto-encoders (SNCAE) is trained to classify the benign or malignant prostates on the basis of the constructed CDFs. Our experiments on 53 clinical DW-MRI data sets resulted in 92.3% accuracy, 83.3% sensitivity, and 100% specificity, indicating that the proposed CAD system could be used as a reliable non-invasive diagnostic tool.

Comparing Three Data Mining Methods to Predict Kidney Transplant Survival

INTRODUCTION

One of the most important complications of post-transplant is rejection. Analyzing survival is one of the areas of medical prognosis and data mining, as an effective approach, has the capacity of analyzing and estimating outcomes in advance through discovering appropriate models among data. The present study aims at comparing the effectiveness of C5.0 algorithms, neural network and C&RTree to predict kidney transplant survival before transplant.

METHOD

To detect factors effective in predicting transplant survival, information needs analysis was performed via a researcher-made questionnaire. A checklist was prepared and data of 513 kidney disease patient files were extracted from Sina Urology Research Center. Following CRISP methodology for data mining, IBM SPSS Modeler 14.2, C5.0, C&RTree algorithms and neural network were used.

RESULTS

Body Mass Index (BMI), cause of renal dysfunction and duration of dialysis were evaluated in all three models as the most effective factors in transplant survival. C5.0 algorithm with the highest validity (96.77%) was the first in estimating kidney transplant survival in patients followed by C&RTree (83.7%) and neural network (79.5%) models.

CONCLUSION

Among the three models, C5.0 algorithm was the top model with high validity that confirms its strength in predicting survival. The most effective kidney transplant survival factors were detected in this study; therefore, duration of transplant survival (year) can be determined considering the regulations set for a new sample with specific characteristics.

Application of wavelet techniques for cancer diagnosis using ultrasound images: A Review

Ultrasound is an important and low cost imaging modality used to study the internal organs of human body and blood flow through blood vessels. It uses high frequency sound waves to acquire images of internal organs. It is used to screen normal, benign and malignant tissues of various organs. Healthy and malignant tissues generate different echoes for ultrasound. Hence, it provides useful information about the potential tumor tissues that can be analyzed for diagnostic purposes before therapeutic procedures. Ultrasound images are affected with speckle noise due to an air gap between the transducer probe and the body. The challenge is to design and develop robust image preprocessing, segmentation and feature extraction algorithms to locate the tumor region and to extract subtle information from isolated tumor region for diagnosis. This information can be revealed using a scale space technique such as the Discrete Wavelet Transform (DWT). It decomposes an image into images at different scales using low pass and high pass filters. These filters help to identify the detail or sudden changes in intensity in the image. These changes are reflected in the wavelet coefficients. Various texture, statistical and image based features can be extracted from these coefficients. The extracted features are subjected to statistical analysis to identify the significant features to discriminate normal and malignant ultrasound images using supervised classifiers. This paper presents a review of wavelet techniques used for preprocessing, segmentation and feature extraction of breast, thyroid, ovarian and prostate cancer using ultrasound images.

Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning

PURPOSE

Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integrate the appearance model into a deformable segmentation framework for prostate MR segmentation.

METHODS

To drive the deformable model for prostate segmentation, the authors propose nonparametric appearance and shape models. The nonparametric appearance model is based on a novel dictionary learning method, namely distributed discriminative dictionary (DDD) learning, which is able to capture fine distinctions in image appearance. To increase the differential power of traditional dictionary-based classification methods, the authors' DDD learning approach takes three strategies. First, two dictionaries for prostate and nonprostate tissues are built, respectively, using the discriminative features obtained from minimum redundancy maximum relevance feature selection. Second, linear discriminant analysis is employed as a linear classifier to boost the optimal separation between prostate and nonprostate tissues, based on the representation residuals from sparse representation. Third, to enhance the robustness of the authors' classification method, multiple local dictionaries are learned for local regions along the prostate boundary (each with small appearance variations), instead of learning one global classifier for the entire prostate. These discriminative dictionaries are located on different patches of the prostate surface and trained to adaptively capture the appearance in different prostate zones, thus achieving better local tissue differentiation. For each local region, multiple classifiers are trained based on the randomly selected samples and finally assembled by a specific fusion method. In addition to this nonparametric appearance model, a prostate shape model is learned from the shape statistics using a novel approach, sparse shape composition, which can model nonGaussian distributions of shape variation and regularize the 3D mesh deformation by constraining it within the observed shape subspace.

RESULTS

The proposed method has been evaluated on two datasets consisting of T2-weighted MR prostate images. For the first (internal) dataset, the classification effectiveness of the authors' improved dictionary learning has been validated by comparing it with three other variants of traditional dictionary learning methods. The experimental results show that the authors' method yields a Dice Ratio of 89.1% compared to the manual segmentation, which is more accurate than the three state-of-the-art MR prostate segmentation methods under comparison. For the second dataset, the MICCAI 2012 challenge dataset, the authors' proposed method yields a Dice Ratio of 87.4%, which also achieves better segmentation accuracy than other methods under comparison.

CONCLUSIONS

A new magnetic resonance image prostate segmentation method is proposed based on the combination of deformable model and dictionary learning methods, which achieves more accurate segmentation performance on prostate T2 MR images.

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.

Medical technologies for the diagnosis of prostate cancer

Prostate cancer is an extremely prevalent problem, especially in our aging population. The prostate-specific antigen test has revolutionized prostate cancer screening. Significant advances have been made in the usage of prostate-specific antigen and its derivatives, biomarkers, diagnostic imaging techniques, biopsy strategy, biopsy needle design and anesthetic agents. Further improvement in prostate cancer detection hinges on the development of an imaging technique that is tumor specific and sensitive to biological aggressiveness.

A survey of prostate segmentation methodologies in ultrasound, magnetic resonance and computed tomography images

Prostate segmentation is a challenging task, and the challenges significantly differ from one imaging modality to another. Low contrast, speckle, micro-calcifications and imaging artifacts like shadow poses serious challenges to accurate prostate segmentation in transrectal ultrasound (TRUS) images. However in magnetic resonance (MR) images, superior soft tissue contrast highlights large variability in shape, size and texture information inside the prostate. In contrast poor soft tissue contrast between prostate and surrounding tissues in computed tomography (CT) images pose a challenge in accurate prostate segmentation. This article reviews the methods developed for prostate gland segmentation TRUS, MR and CT images, the three primary imaging modalities that aids prostate cancer diagnosis and treatment. The objective of this work is to study the key similarities and differences among the different methods, highlighting their strengths and weaknesses in order to assist in the choice of an appropriate segmentation methodology. We define a new taxonomy for prostate segmentation strategies that allows first to group the algorithms and then to point out the main advantages and drawbacks of each strategy. We provide a comprehensive description of the existing methods in all TRUS, MR and CT modalities, highlighting their key-points and features. Finally, a discussion on choosing the most appropriate segmentation strategy for a given imaging modality is provided. A quantitative comparison of the results as reported in literature is also presented.

Automated vs. manual pattern recognition of 3D (1)H MRSI data of patients with prostate cancer

RATIONALE AND OBJECTIVES

The aim of this study was to assess (1) automated analysis methods versus manual evaluation by human experts of three-dimensional proton magnetic resonance spectroscopic imaging (MRSI) data from patients with prostate cancer and (2) the contribution of spatial information to decision making.

MATERIALS AND METHODS

Three-dimensional proton MRSI was applied at 1.5 T. MRSI data from 10 patients with histologically proven prostate adenocarcinoma, scheduled either for prostatectomy or intensity-modulated radiation therapy, were evaluated. First, two readers manually labeled spectra using spatial information to identify the localization of spectra and neighborhood information, establishing the reference set of this study. Then, spectra were labeled again manually in a blinded and randomized manner and evaluated automatically using software that applied spectral line fitting as well as pattern recognition routines. Statistical analysis of the results of the different approaches was performed.

RESULTS

Altogether, 1018 spectra were evaluable by all methods. Numbers of evaluable spectra differed significantly depending on patient and evaluation method. Compared to automated analysis, the readers made rather binary decisions, using information from neighboring spectra in ambiguous cases, when evaluating MRSI data as a whole. Differences between anatomically blinded and unblinded evaluation were larger than differences between evaluations using blinded data and automated techniques.

CONCLUSIONS

An automated approach, which evaluates each spectrum individually, can be as good as an anatomy-blinded human reader. Spatial information is routinely used by human experts to support their final decisions. Automated procedures that consider anatomic information for spectral evaluation will enhance the diagnostic impact of MRSI of the human prostate.

Pattern Discovery from Biological Data

Extracting useful information from structured and unstructured biological data is crucial in the health industry. Some examples include medical practitioner’s need to identify breast cancer patient in the early stage, estimate survival time of a heart disease patient, or recognize uncommon disease characteristics which suddenly appear. Currently there is an explosion in biological data available in the data bases. But information extraction and true open access to data are require time to resolve issues such as ethical clearance. The emergence of novel IT technologies allows health practitioners to facilitate the comprehensive analyses of medical images, genomes, transcriptomes, and proteomes in health and disease. The information that is extracted from such technologies may soon exert a dramatic change in the pace of medical research and impact considerably on the care of patients. The current research will review the existing technologies being used in heart and cancer research. Finally this research will provide some possible solutions to overcome the limitations of existing technologies. In summary the primary objective of this research is to investigate how existing modern machine learning techniques (with their strength and limitations) are being used in the indent of heartbeat related disease and the early detection of cancer in patients. After an extensive literature review these are the objectives chosen: to develop a new approach to find the association between diseases such as high blood pressure, stroke and heartbeat, to propose an improved feature selection method to analyze huge images and microarray databases for machine learning algorithms in cancer research, to find an automatic distance function selection method for clustering tasks, to discover the most significant risk factors for specific cancers, and to determine the preventive factors for specific cancers that are aligned with the most significant risk factors. Therefore we propose a research plan to attain these objectives within this chapter. The possible solutions of the above objectives are: new heartbeat identification techniques show promising association with the heartbeat patterns and diseases, sensitivity based feature selection methods will be applied to early cancer patient classification, meta learning approaches will be adopted in clustering algorithms to select an automatic distance function, and Apriori algorithm will be applied to discover the significant risks and preventive factors for specific cancers. We expect this research will add significant contributions to the medical professional to enable more accurate diagnosis and better patient care. It will also contribute in other area such as biomedical modeling, medical image analysis and early diseases warning.

Contextual computer-aided detection: improving bright lesion detection in retinal images and coronary calcification identification in CT scans

Contextual information plays an important role in medical image understanding. Medical experts make use of context to detect and differentiate pathologies in medical images, especially when interpreting difficult cases. The majority of computer-aided diagnosis (CAD) systems, however, employ only local information to classify candidates, without taking into account global image information or the relation of a candidate with neighboring structures. In this paper, we present a generic system for including contextual information in a CAD system. Context is described by means of high-level features based on the spatial relation between lesion candidates and surrounding anatomical landmarks and lesions of different classes (static contextual features) and lesions of the same type (dynamic contextual features). We demonstrate the added value of contextual CAD for two real-world CAD tasks: the identification of exudates and drusen in 2D retinal images and coronary calcifications in 3D computed tomography scans. Results show that in both applications contextual CAD is superior to a local CAD approach with a significant increase of the figure of merit of the Free Receiver Operating Characteristic curve from 0.84 to 0.92 and from 0.88 to 0.98 for exudates and drusen, respectively, and from 0.87 to 0.93 for coronary calcifications.

Questioning context: a set of interdisciplinary questions for investigating contextual factors affecting health decision making

OBJECTIVE

To combine insights from multiple disciplines into a set of questions that can be used to investigate contextual factors affecting health decision making.

BACKGROUND

Decision-making processes and outcomes may be shaped by a range of non-medical or 'contextual' factors particular to an individual including social, economic, political, geographical and institutional conditions. Research concerning contextual factors occurs across many disciplines and theoretical domains, but few conceptual tools have attempted to integrate and translate this wide-ranging research for health decision-making purposes.

METHODS

To formulate this tool we employed an iterative, collaborative process of scenario development and question generation. Five hypothetical health decision-making scenarios (preventative, screening, curative, supportive and palliative) were developed and used to generate a set of exploratory questions that aim to highlight potential contextual factors across a range of health decisions.

FINDINGS

We present an exploratory tool consisting of questions organized into four thematic domains - Bodies, Technologies, Place and Work (BTPW) - articulating wide-ranging contextual factors relevant to health decision making. The BTPW tool encompasses health-related scholarship and research from a range of disciplines pertinent to health decision making, and identifies concrete points of intersection between its four thematic domains. Examples of the practical application of the questions are also provided.

CONCLUSIONS

These exploratory questions provide an interdisciplinary toolkit for identifying the complex contextual factors affecting decision making. The set of questions comprised by the BTPW tool may be applied wholly or partially in the context of clinical practice, policy development and health-related research.

ProstAtlas: a digital morphologic atlas of the prostate

Computer-aided medical interventions and medical robotics for prostate cancer have known an increasing interest and research activity. However before the routine deployment of these procedures in clinical practice becomes a reality, in vivo and in silico validations must be undertaken. In this study, we developed a digital morphologic atlas of the prostate. We were interested by the gland, the peripheral zone and the central gland. Starting from an image base collected from 30 selected patients, a mean shape and most important deformations for each structure were deduced using principal component analysis. The usefulness of this atlas was highlighted in two applications: image simulation and physical phantom design.

Determining histology-MRI slice correspondences for defining MRI-based disease signatures of prostate cancer

Mapping the spatial disease extent in a certain anatomical organ/tissue from histology images to radiological images is important in defining the disease signature in the radiological images. One such scenario is in the context of men with prostate cancer who have had pre-operative magnetic resonance imaging (MRI) before radical prostatectomy. For these cases, the prostate cancer extent from ex vivo whole-mount histology is to be mapped to in vivo MRI. The need for determining radiology-image-based disease signatures is important for (a) training radiologist residents and (b) for constructing an MRI-based computer aided diagnosis (CAD) system for disease detection in vivo. However, a prerequisite for this data mapping is the determination of slice correspondences (i.e. indices of each pair of corresponding image slices) between histological and magnetic resonance images. The explicit determination of such slice correspondences is especially indispensable when an accurate 3D reconstruction of the histological volume cannot be achieved because of (a) the limited tissue slices with unknown inter-slice spacing, and (b) obvious histological image artifacts (tissue loss or distortion). In the clinic practice, the histology-MRI slice correspondences are often determined visually by experienced radiologists and pathologists working in unison, but this procedure is laborious and time-consuming. We present an iterative method to automatically determine slice correspondence between images from histology and MRI via a group-wise comparison scheme, followed by 2D and 3D registration. The image slice correspondences obtained using our method were compared with the ground truth correspondences determined via consensus of multiple experts over a total of 23 patient studies. In most instances, the results of our method were very close to the results obtained via visual inspection by these experts.

Technologies for localization and diagnosis of prostate cancer

The gold standard for detecting prostate cancer (PCa), systematic biopsy, lacks sensitivity as well as grading accuracy. PSA screening leads to over-treatment of many men, and it is unclear whether screening reduces PCa mortality. This review provides an understanding of the difficulties of localizing and diagnosing PCa. It summarizes recent developments of ultrasound (including elastography) and MRI, and discusses some alternative experimental techniques, such as resonance sensor technology and vibrational spectroscopy. A comparison between the different methods is presented. It is concluded that new ultrasound techniques are promising for targeted biopsy procedures, in order to detect more clinically significant cancers while reducing the number of cores. MRI advances are very promising, but MRI remains expensive and MR-guided biopsy is complex. Resonance sensor technology and vibrational spectroscopy have shown promising results in vitro. There is a need for large prospective multicentre trials that unambiguously prove the clinical benefits of these new techniques.

A multi-classifier system for the characterization of normal, infectious, and cancerous prostate tissues employing transrectal ultrasound images

A computer-aided diagnostic system has been developed for the discrimination of normal, infectious and cancer prostate tissues based on texture analysis of transrectal ultrasound images. The proposed system has been designed using a panel of three classifiers, which have been evaluated individually or as a mutli-classifier scheme, using the external cross-validation procedure. Clinical data consisted of 165 transrectal ultrasound images, characterized by an experienced physician as normal (55/165), cancerous (55/165), and infectious (55/165) prostate cases. From each image, the physician delineated the most representative regions of interest, from which, 23 textural features were extracted. Classification was seen as a two level hierarchical decision tree. Normal from infectious and infectious from cancer cases were discriminated at the 1st and 2nd level of the decision tree, respectively. The best classification results for the 1st level were 89.5%, whereas for the 2nd level 90.1%. The utilization of multi-classifier system improved the discrimination of prostate pathologies as compared to individual classifiers; for infectious prostate cases improvement was from 87.3% to 88.7% and for cancer prostate cases improvement was from 84.1% to 91.4%. In terms of overall system performance (the decision tree's node propagating error taken into account), best classification accuracies were 89.5%, 79.6% and 82.7% for the recognition of normal, infectious and cancer cases, respectively. The proposed system might be used as a second opinion tool for assisting diagnosis of different prostate pathologies.

A fast automatic recognition and location algorithm for fetal genital organs in ultrasound images

Severe sex ratio imbalance at birth is now becoming an important issue in several Asian countries. Its leading immediate cause is prenatal sex-selective abortion following illegal sex identification by ultrasound scanning. In this paper, a fast automatic recognition and location algorithm for fetal genital organs is proposed as an effective method to help prevent ultrasound technicians from unethically and illegally identifying the sex of the fetus. This automatic recognition algorithm can be divided into two stages. In the 'rough' stage, a few pixels in the image, which are likely to represent the genital organs, are automatically chosen as points of interest (POIs) according to certain salient characteristics of fetal genital organs. In the 'fine' stage, a specifically supervised learning framework, which fuses an effective feature data preprocessing mechanism into the multiple classifier architecture, is applied to every POI. The basic classifiers in the framework are selected from three widely used classifiers: radial basis function network, backpropagation network, and support vector machine. The classification results of all the POIs are then synthesized to determine whether the fetal genital organ is present in the image, and to locate the genital organ within the positive image. Experiments were designed and carried out based on an image dataset comprising 658 positive images (images with fetal genital organs) and 500 negative images (images without fetal genital organs). The experimental results showed true positive (TP) and true negative (TN) results from 80.5% (265 from 329) and 83.0% (415 from 500) of samples, respectively. The average computation time was 453 ms per image.

Automatic classification for pathological prostate images based on fractal analysis

Accurate grading for prostatic carcinoma in pathological images is important to prognosis and treatment planning. Since human grading is always time-consuming and subjective, this paper presents a computer-aided system to automatically grade pathological images according to Gleason grading system which is the most widespread method for histological grading of prostate tissues. We proposed two feature extraction methods based on fractal dimension to analyze variations of intensity and texture complexity in regions of interest. Each image can be classified into an appropriate grade by using Bayesian, k-NN, and support vector machine (SVM) classifiers, respectively. Leave-one-out and k-fold cross-validation procedures were used to estimate the correct classification rates (CCR). Experimental results show that 91.2%, 93.7%, and 93.7% CCR can be achieved by Bayesian, k-NN, and SVM classifiers, respectively, for a set of 205 pathological prostate images. If our fractal-based feature set is optimized by the sequential floating forward selection method, the CCR can be promoted up to 94.6%, 94.2%, and 94.6%, respectively, using each of the above three classifiers. Experimental results also show that our feature set is better than the feature sets extracted from multiwavelets, Gabor filters, and gray-level co-occurrence matrix methods because it has a much smaller size and still keeps the most powerful discriminating capability in grading prostate images.

Application of artificial neural networks for decision support in medicine

The emergence of drug resistant pathogens can reduce the efficacy of drugs commonly used to treat infectious diseases. Human immunodeficiency virus (HIV) is particularly sensitive to drug selection pressure, rapidly evolving into drug resistant variants on exposure to anti-HIV drugs. Over 200 mutations within the genetic material of HIV have been shown to be associated with drug resistance to date, and complex mutational patterns have been found in HIV isolates from infected patients exposed to multiple antiretroviral drugs. Genotyping is commonly used in clinical practice as a tool to identify drug resistance mutations in HIV from individual patients. This information is then used to help guide the choice of future therapy for patients whose drug regimen is failing because of the development of drug resistant HIV. Many sets of rules and algorithms are available to predict loss of susceptibility to individual antiretroviral drugs from genotypic data. Although this approach has been helpful, the interpretation of genotypic data remains challenging. We describe here the development and application of ANN models as alternative tools for the interpretation of HIV genotypic drug resistance data. A large amount of clinical and virological data, from around 30,000 patients treated with antiretroviral drugs, has been collected by the HIV Resistance Response Database Initiative (RDI, www.hivrdi.org) in a centralized database. Treatment change episodes (TCEs) have been extracted from these data and used along with HIV drug resistance mutations as the basic input variables to train ANN models. We performed a series of analyses that have helped define the following: (1) the reliability of ANN predictions for HIV patients receiving routine clinical care; (2) the utility of ANN models to identify effective treatments for patients failing therapy; (3) strategies to increase the accuracy of ANN predictions; and (4) performance of ANN models in comparison to the rules-based methods currently in use.

Rapid motion compensation for prostate biopsy using GPU

Image-guided procedures have become routine in medicine. Due to the nature of three-dimensional (3-D) structure of the target organs, two-dimensional (2-D) image acquisition is gradually being replaced by 3-D imaging. Specifically in the diagnosis of prostate cancer, biopsy can be performed using 3-D transrectal ultrasound (TRUS) image guidance. Because prostatic cancers are multifocal, it is crucial to accurately guide biopsy needles towards planned targets. Further the gland tends to move due to external physical disturbances, discomfort introduced by the procedure or intrinsic peristalsis. As a result the exact position of the gland must be rapidly updated so as to correspond with the originally acquired 3-D TRUS volume prior to biopsy planning. A graphics processing unit (GPU) is used in this study to compute rapid updates performing 3-D motion compensation via registration of the live 2-D image and the acquired 3-D TRUS volume. The parallel computational framework on the GPU is exploited resulting in mean compute times of 0.46 seconds for updating the position of a live 2-D buffer image containing 91,000 pixels. A 2x sub-sampling resulted in a further improvement to 0.19 seconds. With the increase in GPU multiprocessors and sub-sampling, we observe that real time motion compensation can be achieved.

Automated detection of a blood pool in ultrasound images of abdominal trauma

Ultrasound imaging is commonly used for emergency diagnosis of blunt trauma. Portable scanners are able to provide adequate imaging in remote and dangerous areas; however, medical expertise may not be available in the immediate local area to interpret the acquired images. The presence of pooled blood in the abdomen is a critical clinical symptom after trauma. This article describes an automated algorithm to detect blood pools in ultrasound images of abdominal trauma. The algorithm creates and uses a feature space consisting of local intensities, averaged local gradient magnitudes and second-order central rotation invariant moments. Successful tests were performed with a set of clinical images of a liver-kidney interface covering the Morrison's pouch, which is the most likely space for blood from an abdominal injury to gather. When implemented in a portable scanner, the reported algorithm will provide rapid, on-the-spot detection of trauma-induced blood pooling and advance notice of a significant blunt traumatic injury.

Targeted prostate biopsy using statistical image analysis

In this paper, a method for maximizing the probability of prostate cancer detection via biopsy is presented, by combining image analysis and optimization techniques. This method consists of three major steps. First, a statistical atlas of the spatial distribution of prostate cancer is constructed from histological images obtained from radical prostatectomy specimen. Second, a probabilistic optimization framework is employed to optimize the biopsy strategy, so that the probability of cancer detection is maximized under needle placement uncertainties. Finally, the optimized biopsy strategy generated in the atlas space is mapped to a specific patient space using an automated segmentation and elastic registration method. Cross-validation experiments showed that the predictive power of the optimized biopsy strategy for cancer detection reached the 94%-96% levels for 6-7 biopsy cores, which is significantly better than standard random-systematic biopsy protocols, thereby encouraging further investigation of optimized biopsy strategies in prospective clinical studies.

Biosensor developments: application to prostate-specific antigen detection

Prostate-specific antigen (PSA) is the best serum marker currently available for the detection of prostate cancer and is the forensic marker of choice for determining the presence of azoospermic semen in some sexual assault cases. Most current assays for PSA detection are processed on large analyzers at dedicated testing sites, which require that samples be sent away for testing. This leads to delays in patient management and increased administration costs. The recent emphasis placed on the need for point-of-care patient management has led to the development of novel biosensor detection strategies that are suitable for the miniaturization of assays for various targets including PSA. This review highlights the current and novel analytical technologies used for PSA detection, which will benefit clinicians, patients and forensic workers in the future.