Cross-Modality Image Registration using a Training-Time Privileged Third Modality

— In this work, we consider the task of pairwise cross-modality image registration, which may benefit from exploiting additional images available only at training time from an additional modality that is different to those being registered. As an example, we focus on aligning intra-subject multiparametric Magnetic Resonance (mpMR) images, between T2-weighted (T2w) scans and diffusionweighted scans with high b-value (DWI_{high−b}). For the application of localising tumours in mpMR images, diffusion scans with zero b-value (DWI_{b=0}) are considered easier to register to T2w due to the availability of corresponding features. We propose a learning from privileged modality algorithm, using a training-only imaging modality DWIb=0, to support the challenging multi-modality registration problems. We present experimental results based on 369 sets of 3D multiparametric MRI images from 356 prostate cancer patients and report, with statistical significance, a lowered median target registration error of 4.34 mm, when registering the holdout DWI_{high−b} and T2w image pairs, compared with that of 7.96 mm before registration. Results also show that the proposed learning-based registration networks enabled efficient registration with comparable or better accuracy, compared with a classical iterative algorithm and other tested learning-based methods with/without the additional modality. These compared algorithms also failed to produce any significantly improved alignment between DWI_{high−b} and T2w in this challenging application

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

MAGNETIC RESONANCE ELASTOGRAPHY FOR APPLICATIONS IN RADIATION THERAPY

Magnetic resonance elastography (MRE) is an imaging technique that combines mechanical waves and magnetic resonance imaging (MRI) to determine the elastic properties of tissue. Because MRE is non-invasive, there is great potential and interest for its use in the detection of cancer. The first part of this thesis concentrates on parameter optimization and imaging quality of an MRE system. To do this, we developed a customized quality assurance phantom, and a series of quality control tests to characterize the MRE system. Our results demonstrated that through optimizing scan parameters, such as frequency and amplitude, MRE could provide a good qualitative elastogram for targets with different elasticity values and dimensions. The second part investigated the feasibility of integrating MRE into radiation therapy (RT) workflow. With the aid of a tissue-equivalent prostate phantom (embedded with three dominant intraprostatic lesions (DILs)), an MRE-integrated RT framework was developed. This framework contains a comprehensive scan protocol including Computed Tomography (CT) scan, combined MRI/MRE scans and a Volumetric Modulated Arc Therapy (VMAT) technique for treatment delivery. The results showed that using the comprehensive information could boost the MRE defined DILs to 84 Gy while keeping the remainder of the prostate to 78 Gy. Using a VMAT based technique allowed us to achieve a highly conformal plan (conformity index for the prostate and combined DILs was 0.98 and 0.91). Based on our feasibility study, we concluded that MRE data can be used for targeted radiation dose escalation. In summary, this thesis demonstrates that MRE is feasible for applications in radiation oncology

Automatic 3D segmentation of the prostate on magnetic resonance images for radiotherapy planning

Abstract. Accurate segmentation of the prostate, the seminal vesicles, the bladder and the rectum is a crucial step for planning radiotherapy (RT) procedures. Modern radiotherapy protocols have included the delineation of the pelvic organs in magnetic resonance images (MRI), as the guide to the therapeutic beam irradiation over the target organ. However, this task is highly inter and intra-expert variable and may take about 20 minutes per patient, even for trained experts, constituting an important burden in most radiological services. Automatic or semi-automatic segmentation strategies might then improve the efficiency by decreasing the measured times while conserving the required accuracy. This thesis presents a fully automatic prostate segmentation framework that selects the most similar prostates w.r.t. a test prostate image and combines them to estimate the segmentation for the test prostate. A robust multi-scale analysis establishes the set of most similar prostates from a database, independently of the acquisition protocol. Those prostates are then non-rigidly registered towards the test image and fusioned by a linear combination. The proposed approach was evaluated using a MRI public dataset of patients with benign hyperplasia or cancer, following different acquisition protocols, namely 26 endorectal and 24 external. Evaluating under a leave-one-out scheme, results show reliable segmentations, obtaining an average dice coefficient of 79%, when comparing with the expert manual segmentation.La delineación exacta de la próstata, las vesículas seminales, la vejiga y el recto es un paso fundamental para el planeamiento de procedimientos de radioterapia. Protocolos modernos han incluido la delineación de los órganos pélvicos en imágenes de resonancia magnética (IRM), como la guia para la irradiación del haz terapéutico sobre el órgano objetivo. Sin embargo, esta tarea es altamente variable intra e inter-experto y puede tomar al rededor de 20 minutos por paciente, incluso para expertos entrenados, convirtiéndose en una carga importante en la mayoría de los servicios de radiología. Métodos automáticos o semi-automáticos podrían mejorar la eficiencia disminuyendo los tiempos medidos mientras se conserva la precisión requerida. Este trabajo presenta una estrategia de segmentación de la próstata completamente automático que selecciona las prostatas más similares con respecto a una imagen de resonancia magnética de prueba y combina las delineaciones asociadas a dichas imágenes para estimar la segmentación de la imagen de prueba. Un análisis multiescala robusto permite establecer el conjunto de las próstatas más parecidas de una base de datos, independiente del protocolo de adquisición. Las imágenes seleccionadas son registradas de forma no rigida con respecto a la imagen de prueba y luego son fusionadas mediante una combinación lineal. El enfoque propuesto fue evaluado utilizando un conjunto público de imágenes de resonancia magnética de pacientes con hiperplasia benigna o con cancer, con diferentes protocolos de adquisición, esto es 26 externas y 24 endorectales. Este trabajo fue evaluado bajo un esquema leave-one-out, cuyos resultados mostraron segmentaciones confiables, obteniendo un DSC promedio de 79%, cuando se compararon los resultados obtenidos con las segmentaciones manuales de expertos.Maestrí

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

Current Approaches for Image Fusion of Histological Data with Computed Tomography and Magnetic Resonance Imaging

Classical analysis of biological samples requires the destruction of the tissue’s integrity by cutting or grinding it down to thin slices for (Immuno)-histochemical staining and microscopic analysis. Despite high specificity, encoded in the stained 2D section of the whole tissue, the structural information, especially 3D information, is limited. Computed tomography (CT) or magnetic resonance imaging (MRI) scans performed prior to sectioning in combination with image registration algorithms provide an opportunity to regain access to morphological characteristics as well as to relate histological findings to the 3D structure of the local tissue environment. This review provides a summary of prevalent literature addressing the problem of multimodal coregistration of hard- and soft-tissue in microscopy and tomography. Grouped according to the complexity of the dimensions, including image-to-volume (2D ⟶ 3D), image-to-image (2D ⟶ 2D), and volume-to-volume (3D ⟶ 3D), selected currently applied approaches are investigated by comparing the method accuracy with respect to the limiting resolution of the tomography. Correlation of multimodal imaging could position itself as a useful tool allowing for precise histological diagnostic and allow the a priori planning of tissue extraction like biopsies

Finite Element Based Tracking of Deforming Surfaces

We present an approach to robustly track the geometry of an object that deforms over time from a set of input point clouds captured from a single viewpoint. The deformations we consider are caused by applying forces to known locations on the object's surface. Our method combines the use of prior information on the geometry of the object modeled by a smooth template and the use of a linear finite element method to predict the deformation. This allows the accurate reconstruction of both the observed and the unobserved sides of the object. We present tracking results for noisy low-quality point clouds acquired by either a stereo camera or a depth camera, and simulations with point clouds corrupted by different error terms. We show that our method is also applicable to large non-linear deformations.Comment: additional experiment

Deformable MRI to Transrectal Ultrasound Registration for Prostate Interventions Using Deep Learning

RÉSUMÉ: Le cancer de la prostate est l’un des principaux problèmes de santé publique dans le monde. Un diagnostic précoce du cancer de la prostate pourrait jouer un rôle vital dans le traitement des patients. Les procédures de biopsie sont utilisées à des fins de diagnostic. À cet égard, l’échographie transrectale (TRUS) est considérée comme un standard pour l’imagerie de la prostate lors d’une biopsie ou d’une curiethérapie. Cette technique d’imagerie est relativement peu coûteuse, peut scanner l’organe en temps réel et est sans radiation. Ainsi, les scans TRUS sont utilisés pour guider les cliniciens sur l’emplacement d’une tumeur à l’intérieur de la prostate. Le défi majeur réside dans le fait que les images TRUS ont une faible résolution et qualité d’image. Il est difficile de distinguer l’emplacement exact de la tumeur et l’étendue de la maladie. De plus, l’organe de la prostate subit d’importantes variations de forme au cours d’une intervention de la prostate, ce qui rend l’identification de la tumeur encore plus difficile.----------ABSTRACT: Prostate cancer is one of the major public health issues in the world. An accurate and early diagnosis of prostate cancer could play a vital role in the treatment of patients. Biopsy procedures are used for diagnosis purposes. In this regard, Transrectal Ultrasound (TRUS) is considered a standard for imaging the prostate during a biopsy or brachytherapy procedure. This imaging technique is comparatively low-cost, can scan the organ in real-time, and is radiation free. Thus, TRUS scans are used to guide the clinicians about the location of a tumor inside the prostate organ. The major challenge lies in the fact that TRUS images have low resolution and quality. This makes it difficult to distinguish the exact tumor location and the extent of the disease. In addition, the prostate organ undergoes important shape variations during a prostate intervention procedure, which makes the tumor identification even harder

Validation Strategies Supporting Clinical Integration of Prostate Segmentation Algorithms for Magnetic Resonance Imaging

Segmentation of the prostate in medical images is useful for prostate cancer diagnosis and therapy guidance. However, manual segmentation of the prostate is laborious and time-consuming, with inter-observer variability. The focus of this thesis was on accuracy, reproducibility and procedure time measurement for prostate segmentation on T2-weighted endorectal magnetic resonance imaging, and assessment of the potential of a computer-assisted segmentation technique to be translated to clinical practice for prostate cancer management. We collected an image data set from prostate cancer patients with manually-delineated prostate borders by one observer on all the images and by two other observers on a subset of images. We used a complementary set of error metrics to measure the different types of observed segmentation errors. We compared expert manual segmentation as well as semi-automatic and automatic segmentation approaches before and after manual editing by expert physicians. We recorded the time needed for user interaction to initialize the semi-automatic algorithm, algorithm execution, and manual editing as necessary. Comparing to manual segmentation, the measured errors for the algorithms compared favourably with observed differences between manual segmentations. The measured average editing times for the computer-assisted segmentation were lower than fully manual segmentation time, and the algorithms reduced the inter-observer variability as compared to manual segmentation. The accuracy of the computer-assisted approaches was near to or within the range of observed variability in manual segmentation. The recorded procedure time for prostate segmentation was reduced using computer-assisted segmentation followed by manual editing, compared to the time required for fully manual segmentation