A non-invasive image based system for early diagnosis of prostate cancer.

Prostate cancer is the second most fatal cancer experienced by American males. The average American male has a 16.15% chance of developing prostate cancer, which is 8.38% higher than lung cancer, the second most likely cancer. The current in-vitro techniques that are based on analyzing a patients blood and urine have several limitations concerning their accuracy. In addition, the prostate Specific Antigen (PSA) blood-based test, has a high chance of false positive diagnosis, ranging from 28%-58%. Yet, biopsy remains the gold standard for the assessment of prostate cancer, but only as the last resort because of its invasive nature, high cost, and potential morbidity rates. The major limitation of the relatively small needle biopsy samples is the higher possibility of producing false positive diagnosis. Moreover, the visual inspection system (e.g., Gleason grading system) is not quantitative technique and different observers may classify a sample differently, leading to discrepancies in the diagnosis. As reported in the literature that the early detection of prostate cancer is a crucial step for decreasing prostate cancer related deaths. Thus, there is an urgent need for developing objective, non-invasive image based technology for early detection of prostate cancer. The objective of this dissertation is to develop a computer vision methodology, later translated into a clinically usable software tool, which can improve sensitivity and specificity of early prostate cancer diagnosis based on the well-known hypothesis that malignant tumors are will connected with the blood vessels than the benign tumors. Therefore, using either Diffusion Weighted Magnetic Resonance imaging (DW-MRI) or Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI), we will be able to interrelate the amount of blood in the detected prostate tumors by estimating either the Apparent Diffusion Coefficient (ADC) in the prostate with the malignancy of the prostate tumor or perfusion parameters. We intend to validate this hypothesis by demonstrating that automatic segmentation of the prostate from either DW-MRI or DCE-MRI after handling its local motion, provides discriminatory features for early prostate cancer diagnosis. The proposed CAD system consists of three majors components, the first two of which constitute new research contributions to a challenging computer vision problem. The three main components are: (1) A novel Shape-based segmentation approach to segment the prostate from either low contrast DW-MRI or DCE-MRI data; (2) A novel iso-contours-based non-rigid registration approach to ensure that we have voxel-on-voxel matches of all data which may be more difficult due to gross patient motion, transmitted respiratory effects, and intrinsic and transmitted pulsatile effects; and (3) Probabilistic models for the estimated diffusion and perfusion features for both malignant and benign tumors. Our results showed a 98% classification accuracy using Leave-One-Subject-Out (LOSO) approach based on the estimated ADC for 30 patients (12 patients diagnosed as malignant; 18 diagnosed as benign). These results show the promise of the proposed image-based diagnostic technique as a supplement to current technologies for diagnosing prostate cancer

Analysis of contrast-enhanced medical images.

Early detection of human organ diseases is of great importance for the accurate diagnosis and institution of appropriate therapies. This can potentially prevent progression to end-stage disease by detecting precursors that evaluate organ functionality. In addition, it also assists the clinicians for therapy evaluation, tracking diseases progression, and surgery operations. Advances in functional and contrast-enhanced (CE) medical images enabled accurate noninvasive evaluation of organ functionality due to their ability to provide superior anatomical and functional information about the tissue-of-interest. The main objective of this dissertation is to develop a computer-aided diagnostic (CAD) system for analyzing complex data from CE magnetic resonance imaging (MRI). The developed CAD system has been tested in three case studies: (i) early detection of acute renal transplant rejection, (ii) evaluation of myocardial perfusion in patients with ischemic heart disease after heart attack; and (iii), early detection of prostate cancer. However, developing a noninvasive CAD system for the analysis of CE medical images is subject to multiple challenges, including, but are not limited to, image noise and inhomogeneity, nonlinear signal intensity changes of the images over the time course of data acquisition, appearances and shape changes (deformations) of the organ-of-interest during data acquisition, determination of the best features (indexes) that describe the perfusion of a contrast agent (CA) into the tissue. To address these challenges, this dissertation focuses on building new mathematical models and learning techniques that facilitate accurate analysis of CAs perfusion in living organs and include: (i) accurate mathematical models for the segmentation of the object-of-interest, which integrate object shape and appearance features in terms of pixel/voxel-wise image intensities and their spatial interactions; (ii) motion correction techniques that combine both global and local models, which exploit geometric features, rather than image intensities to avoid problems associated with nonlinear intensity variations of the CE images; (iii) fusion of multiple features using the genetic algorithm. The proposed techniques have been integrated into CAD systems that have been tested in, but not limited to, three clinical studies. First, a noninvasive CAD system is proposed for the early and accurate diagnosis of acute renal transplant rejection using dynamic contrast-enhanced MRI (DCE-MRI). Acute rejection–the immunological response of the human immune system to a foreign kidney–is the most sever cause of renal dysfunction among other diagnostic possibilities, including acute tubular necrosis and immune drug toxicity. In the U.S., approximately 17,736 renal transplants are performed annually, and given the limited number of donors, transplanted kidney salvage is an important medical concern. Thus far, biopsy remains the gold standard for the assessment of renal transplant dysfunction, but only as the last resort because of its invasive nature, high cost, and potential morbidity rates. The diagnostic results of the proposed CAD system, based on the analysis of 50 independent in-vivo cases were 96% with a 95% confidence interval. These results clearly demonstrate the promise of the proposed image-based diagnostic CAD system as a supplement to the current technologies, such as nuclear imaging and ultrasonography, to determine the type of kidney dysfunction. Second, a comprehensive CAD system is developed for the characterization of myocardial perfusion and clinical status in heart failure and novel myoregeneration therapy using cardiac first-pass MRI (FP-MRI). Heart failure is considered the most important cause of morbidity and mortality in cardiovascular disease, which affects approximately 6 million U.S. patients annually. Ischemic heart disease is considered the most common underlying cause of heart failure. Therefore, the detection of the heart failure in its earliest forms is essential to prevent its relentless progression to premature death. While current medical studies focus on detecting pathological tissue and assessing contractile function of the diseased heart, this dissertation address the key issue of the effects of the myoregeneration therapy on the associated blood nutrient supply. Quantitative and qualitative assessment in a cohort of 24 perfusion data sets demonstrated the ability of the proposed framework to reveal regional perfusion improvements with therapy, and transmural perfusion differences across the myocardial wall; thus, it can aid in follow-up on treatment for patients undergoing the myoregeneration therapy. Finally, an image-based CAD system for early detection of prostate cancer using DCE-MRI is introduced. Prostate cancer is the most frequently diagnosed malignancy among men and remains the second leading cause of cancer-related death in the USA with more than 238,000 new cases and a mortality rate of about 30,000 in 2013. Therefore, early diagnosis of prostate cancer can improve the effectiveness of treatment and increase the patient’s chance of survival. Currently, needle biopsy is the gold standard for the diagnosis of prostate cancer. However, it is an invasive procedure with high costs and potential morbidity rates. Additionally, it has a higher possibility of producing false positive diagnosis due to relatively small needle biopsy samples. Application of the proposed CAD yield promising results in a cohort of 30 patients that would, in the near future, represent a supplement of the current technologies to determine prostate cancer type. The developed techniques have been compared to the state-of-the-art methods and demonstrated higher accuracy as shown in this dissertation. The proposed models (higher-order spatial interaction models, shape models, motion correction models, and perfusion analysis models) can be used in many of today’s CAD applications for early detection of a variety of diseases and medical conditions, and are expected to notably amplify the accuracy of CAD decisions based on the automated analysis of CE images

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Segmentation of pelvic structures from preoperative images for surgical planning and guidance

Prostate cancer is one of the most frequently diagnosed malignancies globally and the second leading cause of cancer-related mortality in males in the developed world. In recent decades, many techniques have been proposed for prostate cancer diagnosis and treatment. With the development of imaging technologies such as CT and MRI, image-guided procedures have become increasingly important as a means to improve clinical outcomes. Analysis of the preoperative images and construction of 3D models prior to treatment would help doctors to better localize and visualize the structures of interest, plan the procedure, diagnose disease and guide the surgery or therapy. This requires efficient and robust medical image analysis and segmentation technologies to be developed. The thesis mainly focuses on the development of segmentation techniques in pelvic MRI for image-guided robotic-assisted laparoscopic radical prostatectomy and external-beam radiation therapy. A fully automated multi-atlas framework is proposed for bony pelvis segmentation in MRI, using the guidance of MRI AE-SDM. With the guidance of the AE-SDM, a multi-atlas segmentation algorithm is used to delineate the bony pelvis in a new \ac{MRI} where there is no CT available. The proposed technique outperforms state-of-the-art algorithms for MRI bony pelvis segmentation. With the SDM of pelvis and its segmented surface, an accurate 3D pelvimetry system is designed and implemented to measure a comprehensive set of pelvic geometric parameters for the examination of the relationship between these parameters and the difficulty of robotic-assisted laparoscopic radical prostatectomy. This system can be used in both manual and automated manner with a user-friendly interface. A fully automated and robust multi-atlas based segmentation has also been developed to delineate the prostate in diagnostic MR scans, which have large variation in both intensity and shape of prostate. Two image analysis techniques are proposed, including patch-based label fusion with local appearance-specific atlases and multi-atlas propagation via a manifold graph on a database of both labeled and unlabeled images when limited labeled atlases are available. The proposed techniques can achieve more robust and accurate segmentation results than other multi-atlas based methods. The seminal vesicles are also an interesting structure for therapy planning, particularly for external-beam radiation therapy. As existing methods fail for the very onerous task of segmenting the seminal vesicles, a multi-atlas learning framework via random decision forests with graph cuts refinement has further been proposed to solve this difficult problem. Motivated by the performance of this technique, I further extend the multi-atlas learning to segment the prostate fully automatically using multispectral (T1 and T2-weighted) MR images via hybrid \ac{RF} classifiers and a multi-image graph cuts technique. The proposed method compares favorably to the previously proposed multi-atlas based prostate segmentation. The work in this thesis covers different techniques for pelvic image segmentation in MRI. These techniques have been continually developed and refined, and their application to different specific problems shows ever more promising results.Open Acces

Segmentation of the Prostatic Gland and the Intraprostatic Lesions on Multiparametic Magnetic Resonance Imaging Using Mask Region-Based Convolutional Neural Networks

Purpose: Accurate delineation of the prostate gland and intraprostatic lesions (ILs) is essential for prostate cancer dose-escalated radiation therapy. The aim of this study was to develop a sophisticated deep neural network approach to magnetic resonance image analysis that will help IL detection and delineation for clinicians. Methods and Materials: We trained and evaluated mask region-based convolutional neural networks to perform the prostate gland and IL segmentation. There were 2 cohorts in this study: 78 public patients (cohort 1) and 42 private patients from our institution (cohort 2). Prostate gland segmentation was performed using T2-weighted images (T2WIs), although IL segmentation was performed using T2WIs and coregistered apparent diffusion coefficient maps with prostate patches cropped out. The IL segmentation model was extended to select 5 highly suspicious volumetric lesions within the entire prostate. Results: The mask region-based convolutional neural networks model was able to segment the prostate with dice similarity coefficient (DSC) of 0.88 ± 0.04, 0.86 ± 0.04, and 0.82 ± 0.05; sensitivity (Sens.) of 0.93, 0.95, and 0.95; and specificity (Spec.) of 0.98, 0.85, and 0.90. However, ILs were segmented with DSC of 0.62 ± 0.17, 0.59 ± 0.14, and 0.38 ± 0.19; Sens. of 0.55 ± 0.30, 0.63 ± 0.28, and 0.22 ± 0.24; and Spec. of 0.974 ± 0.010, 0.964 ± 0.015, and 0.972 ± 0.015 in public validation/public testing/private testing patients when trained with patients from cohort 1 only. When trained with patients from both cohorts, the values were as follows: DSC of 0.64 ± 0.11, 0.56 ± 0.15, and 0.46 ± 0.15; Sens. of 0.57 ± 0.23, 0.50 ± 0.28, and 0.33 ± 0.17; and Spec. of 0.980 ± 0.009, 0.969 ± 0.016, and 0.977 ± 0.013. Conclusions: Our research framework is able to perform as an end-to-end system that automatically segmented the prostate gland and identified and delineated highly suspicious ILs within the entire prostate. Therefore, this system demonstrated the potential for assisting the clinicians in tumor delineation

Medical Image Registration Using Deep Neural Networks

Registration is a fundamental problem in medical image analysis wherein images are transformed spatially to align corresponding anatomical structures in each image. Recently, the development of learning-based methods, which exploit deep neural networks and can outperform classical iterative methods, has received considerable interest from the research community. This interest is due in part to the substantially reduced computational requirements that learning-based methods have during inference, which makes them particularly well-suited to real-time registration applications. Despite these successes, learning-based methods can perform poorly when applied to images from different modalities where intensity characteristics can vary greatly, such as in magnetic resonance and ultrasound imaging. Moreover, registration performance is often demonstrated on well-curated datasets, closely matching the distribution of the training data. This makes it difficult to determine whether demonstrated performance accurately represents the generalization and robustness required for clinical use. This thesis presents learning-based methods which address the aforementioned difficulties by utilizing intuitive point-set-based representations, user interaction and meta-learning-based training strategies. Primarily, this is demonstrated with a focus on the non-rigid registration of 3D magnetic resonance imaging to sparse 2D transrectal ultrasound images to assist in the delivery of targeted prostate biopsies. While conventional systematic prostate biopsy methods can require many samples to be taken to confidently produce a diagnosis, tumor-targeted approaches have shown improved patient, diagnostic, and disease management outcomes with fewer samples. However, the available intraoperative transrectal ultrasound imaging alone is insufficient for accurate targeted guidance. As such, this exemplar application is used to illustrate the effectiveness of sparse, interactively-acquired ultrasound imaging for real-time, interventional registration. The presented methods are found to improve registration accuracy, relative to state-of-the-art, with substantially lower computation time and require a fraction of the data at inference. As a result, these methods are particularly attractive given their potential for real-time registration in interventional applications

A non-invasive diagnostic system for early assessment of acute renal transplant rejection.

Early diagnosis of acute renal transplant rejection (ARTR) is of immense importance for appropriate therapeutic treatment administration. Although the current diagnostic technique is based on renal biopsy, it is not preferred due to its invasiveness, recovery time (1-2 weeks), and potential for complications, e.g., bleeding and/or infection. In this thesis, a computer-aided diagnostic (CAD) system for early detection of ARTR from 4D (3D + b-value) diffusion-weighted (DW) MRI data is developed. The CAD process starts from a 3D B-spline-based data alignment (to handle local deviations due to breathing and heart beat) and kidney tissue segmentation with an evolving geometric (level-set-based) deformable model. The latter is guided by a voxel-wise stochastic speed function, which follows from a joint kidney-background Markov-Gibbs random field model accounting for an adaptive kidney shape prior and for on-going visual kidney-background appearances. A cumulative empirical distribution of apparent diffusion coefficient (ADC) at different b-values of the segmented DW-MRI is considered a discriminatory transplant status feature. Finally, a classifier based on deep learning of a non-negative constrained stacked auto-encoder is employed to distinguish between rejected and non-rejected renal transplants. In the “leave-one-subject-out” experiments on 53 subjects, 98% of the subjects were correctly classified (namely, 36 out of 37 rejected transplants and 16 out of 16 nonrejected ones). Additionally, a four-fold cross-validation experiment was performed, and an average accuracy of 96% was obtained. These experimental results hold promise of the proposed CAD system as a reliable non-invasive diagnostic tool

A novel NMF-based DWI CAD framework for prostate cancer.

In this thesis, a computer aided diagnostic (CAD) framework for detecting prostate cancer in DWI data is proposed. The proposed CAD method consists of two frameworks that use nonnegative matrix factorization (NMF) to learn meaningful features from sets of high-dimensional data. The first technique, is a three dimensional (3D) level-set DWI prostate segmentation algorithm guided by a novel probabilistic speed function. This speed function is driven by the features learned by NMF from 3D appearance, shape, and spatial data. The second technique, is a probabilistic classifier that seeks to label a prostate segmented from DWI data as either alignat, contain cancer, or benign, containing no cancer. This approach uses a NMF-based feature fusion to create a feature space where data classes are clustered. In addition, using DWI data acquired at a wide range of b-values (i.e. magnetic field strengths) is investigated. Experimental analysis indicates that for both of these frameworks, using NMF producing more accurate segmentation and classification results, respectively, and that combining the information from DWI data at several b-values can assist in detecting prostate cancer