Active Contouring Based on Gradient Vector Interaction and Constrained Level Set Diffusion

This paper addresses an important issue in deformable modeling: initialisation dependency for edge-based models. To the best of our knowledge, this is the first edge-based deformable method that can achieve practical initialisation invariance. It can handle more sophisticated topological changes than splitting and merging. It provides new potentials for edge-based active contour methods, particularly when detecting and localising objects with unknown location, geometry, and topology

Dynamical models and machine learning for supervised segmentation

This thesis is concerned with the problem of how to outline regions of interest in medical images, when the boundaries are weak or ambiguous and the region shapes are irregular. The focus on machine learning and interactivity leads to a common theme of the need to balance conflicting requirements. First, any machine learning method must strike a balance between how much it can learn and how well it generalises. Second, interactive methods must balance minimal user demand with maximal user control. To address the problem of weak boundaries,methods of supervised texture classification are investigated that do not use explicit texture features. These methods enable prior knowledge about the image to benefit any segmentation framework. A chosen dynamic contour model, based on probabilistic boundary tracking, combines these image priors with efficient modes of interaction. We show the benefits of the texture classifiers over intensity and gradient-based image models, in both classification and boundary extraction. To address the problem of irregular region shape, we devise a new type of statistical shape model (SSM) that does not use explicit boundary features or assume high-level similarity between region shapes. First, the models are used for shape discrimination, to constrain any segmentation framework by way of regularisation. Second, the SSMs are used for shape generation, allowing probabilistic segmentation frameworks to draw shapes from a prior distribution. The generative models also include novel methods to constrain shape generation according to information from both the image and user interactions. The shape models are first evaluated in terms of discrimination capability, and shown to out-perform other shape descriptors. Experiments also show that the shape models can benefit a standard type of segmentation algorithm by providing shape regularisers. We finally show how to exploit the shape models in supervised segmentation frameworks, and evaluate their benefits in user trials

Quantitative DWI as an Early Imaging Biomarker of the Response to Chemoradiation in Esophageal Cancer

For patients diagnosed with stages IIa-IIb esophageal cancer, the current standard of care treatment is tri-modality therapy (TMT), where neoadjuvant chemoradiation (nCRT) is followed by surgical resection. Histopathology of resected tumors reveals that pathological complete response (pCR) is achieved in 20-30% of patients through nCRT alone. Because of the high mortality and morbidity associated with esophagectomy, it may be advantageous for patients exhibiting pCR from nCRT alone to be placed under observation rather than completing their TMT. Therefore, a method for predicting response at an early time-point during nCRT is highly desirable. Conventional methods such as endoscopic ultrasound, re-biopsy, and morphologic imaging are insufficient for this purpose. During nCRT, morphologic changes in tumors are often preceded by changes in the tumor biology. Diffusion Weighed Imaging (DWI) is an MRI modality which is sensitive to microscopic motion of water molecules in tissue. Quantitative DWI provides a measure of the cellular microenvironment which is impacted by cellularity, extra-cellular volume fraction, structure of the extracellular matrix, and cellular membranes. This work sought to investigate if changes in quantitative DWI may be used as an early imaging biomarker for the prediction of response to nCRT in esophageal cancer. DWI scans were performed on a small group of esophageal cancer patients (stages IIa to IIIb) before, at interim, and after completion of their nCRT. Quantitative diffusion parameter maps were estimated for DWI scans using the following models of diffusion: mono-exponential, intra-voxel incoherent motion (IVIM), and kurtosis. Summary measures of quantitative diffusion parameters were extracted from tumor voxels through volumetric contouring. These summary measures were retrospectively compared between histopathologically confirmed groupings of patients as pCR and non-pCR. The study found that the relative change in mean ADC could completely separate groupings of pCR and non-pCR patients (AUC=1) at a cutoff of 27.7%. Measurement by volume contouring was shown to be highly reproducible between readers. This pilot study demonstrates the promise of using DWI for organ sparing approaches after nCRT in esophageal cancer

A level set method with Sobolev Gradient and Haralick Edge Detection

Variational level set methods, which have been proposed with various energy functionals, mostly use the ordinary L type gradient in gradient descent algorithm to minimize the energy functional. The gradient flow is influenced by both the energy to be minimized and the norms, which are induced from inner products, used to measure the cost of perturbation of the curve. However, there are many undesired properties related to the gradient flows due to the 2 L type inner products. For example, there is not any regularity term in the definition of this inner product that causes non-smooth flows and inaccurate results. Therefore, in this work, Sobolev gradient has been used that is more efficient than the 2 L type gradient for image segmentation and has powerful properties such as regular gradient flows, independency to parameterization of curves, less sensitive to local features and noise in the image and also faster convergence rate than the standard gradient. In addition, Haralick edge detector has been used instead of the edge indicator function in this study. Because, the traditional edge indicator function, which is the absolute of the gradient of the convolved image with the aussian function, is sensitive to noise in level set methods. Experimental results on real images , which are abdominal magnetic resonance images, have been obtained for spleen and kidney segmentation. Quantitative analyses have been performed by using different measurements to evaluate the performance of the proposed approach, which can ignore topological noises and detect boundaries successfully

Optimization of a boundary element approach to electromagnet design with application to a host of current problems in Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) has proven to be a valuable methodological approach in both basic research and clinical practice. However, significant hardware advances are still needed in order to further improve and extend the applications of the technique. The present dissertation predominantly addresses gradient and shim coil design (sub-systems of the MR system). A design study to investigate gradient performance over a set of surface geometries ranging in curvature from planar to a full cylinder using the boundary element (BE) method is presented. The results of this study serve as a guide for future planar and pseudo-planar gradient systems for a range of applications. Additions to the BE method of coil design are developed, including the direct control of the magnetic field uniformity produced by the final electromagnet and the minimum separation between adjacent wires in the final design. A method to simulate induced eddy currents on thin conducting surfaces is presented. The method is used to predict the time-dependent decay of eddy currents induced on a cylindrical copper bore within a 7 T MR system and the induced heating on small conducting structures; both predictions are compared against experiment. Next, the method is extended to predict localized power deposition and the spatial distribution of force due to the Lorentz interaction of the eddy current distribution with the main magnetic field. New methods for the design of actively shielded electromagnets are presented and compared with existing techniques for the case of a whole-body transverse gradient coil. The methods are judged using a variety of shielding performance parameters. A novel approach to eliminate the interactions between the MR gradient system and external, non-MR specific, active devices is presented and its feasibility is discussed. A completely new approach to shimming is presented utilizing a network of current pathways that can be adaptively changed on a subject-by-subject basis and dynamically controlled. The potential benefits of the approach are demonstrated using computer simulations and a prototype coil is constructed and tested as a proof-of-principle

Initial contour generation approach in level set methods for dental image segmentation

Segmentation is challenging process in medical images especially on dental x-ray images. Level set methods have effective result on medical and dental image segmentation. Initial Contour (IC) is the essential step in level set image segmentation methods due to start the efficient process. However, the main issue with IC is how to generate the automatic technique in order to reduce the human interaction and moreover, suitable IC to have accurate result. In this paper a new region-based technique for IC generation, is proposed to overcome this issue. The idea is to generate the most suitable IC since the manual initialization of the level set function surface is a well-known drawback for accurate segmentation which has dependency on selection of IC and wrong selection will affect the result. We have utilized the statistical and morphological information inside and outside the contour to establish a region-based map function. This function is able to find the suitable IC on images to perform by level set methods. Experiments on dental x-ray images demonstrate the robustness of segmentation process using proposed method even on noisy images and with weak boundary. Furthermore, computational cost of segmentation process will be reduced

3D fusion of histology to multi-parametric MRI for prostate cancer imaging evaluation and lesion-targeted treatment planning

Multi-parametric magnetic resonance imaging (mpMRI) of localized prostate cancer has the potential to support detection, staging and localization of tumors, as well as selection, delivery and monitoring of treatments. Delineating prostate cancer tumors on imaging could potentially further support the clinical workflow by enabling precise monitoring of tumor burden in active-surveillance patients, optimized targeting of image-guided biopsies, and targeted delivery of treatments to decrease morbidity and improve outcomes. Evaluating the performance of mpMRI for prostate cancer imaging and delineation ideally includes comparison to an accurately registered reference standard, such as prostatectomy histology, for the locations of tumor boundaries on mpMRI. There are key gaps in knowledge regarding how to accurately register histological reference standards to imaging, and consequently further gaps in knowledge regarding the suitability of mpMRI for tasks, such as tumor delineation, that require such reference standards for evaluation. To obtain an understanding of the magnitude of the mpMRI-histology registration problem, we quantified the position, orientation and deformation of whole-mount histology sections relative to the formalin-fixed tissue slices from which they were cut. We found that (1) modeling isotropic scaling accounted for the majority of the deformation with a further small but statistically significant improvement from modeling affine transformation, and (2) due to the depth (mean±standard deviation (SD) 1.1±0.4 mm) and orientation (mean±SD 1.5±0.9°) of the sectioning, the assumption that histology sections are cut from the front faces of tissue slices, common in previous approaches, introduced a mean error of 0.7 mm. To determine the potential consequences of seemingly small registration errors such as described above, we investigated the impact of registration accuracy on the statistical power of imaging validation studies using a co-registered spatial reference standard (e.g. histology images) by deriving novel statistical power formulae that incorporate registration error. We illustrated, through a case study modeled on a prostate cancer imaging trial at our centre, that submillimeter differences in registration error can have a substantial impact on the required sample sizes (and therefore also the study cost) for studies aiming to detect mpMRI signal differences due to 0.5 – 2.0 cm3 prostate tumors. With the aim of achieving highly accurate mpMRI-histology registrations without disrupting the clinical pathology workflow, we developed a three-stage method for accurately registering 2D whole-mount histology images to pre-prostatectomy mpMRI that allowed flexible placement of cuts during slicing for pathology and avoided the assumption that histology sections are cut from the front faces of tissue slices. The method comprised a 3D reconstruction of histology images, followed by 3D–3D ex vivo–in vivo and in vivo–in vivo image transformations. The 3D reconstruction method minimized fiducial registration error between cross-sections of non-disruptive histology- and ex-vivo-MRI-visible strand-shaped fiducials to reconstruct histology images into the coordinate system of an ex vivo MR image. We quantified the mean±standard deviation target registration error of the reconstruction to be 0.7±0.4 mm, based on the post-reconstruction misalignment of intrinsic landmark pairs. We also compared our fiducial-based reconstruction to an alternative reconstruction based on mutual-information-based registration, an established method for multi-modality registration. We found that the mean target registration error for the fiducial-based method (0.7 mm) was lower than that for the mutual-information-based method (1.2 mm), and that the mutual-information-based method was less robust to initialization error due to multiple sources of error, including the optimizer and the mutual information similarity metric. The second stage of the histology–mpMRI registration used interactively defined 3D–3D deformable thin-plate-spline transformations to align ex vivo to in vivo MR images to compensate for deformation due to endorectal MR coil positioning, surgical resection and formalin fixation. The third stage used interactively defined 3D–3D rigid or thin-plate-spline transformations to co-register in vivo mpMRI images to compensate for patient motion and image distortion. The combined mean registration error of the histology–mpMRI registration was quantified to be 2 mm using manually identified intrinsic landmark pairs. Our data set, comprising mpMRI, target volumes contoured by four observers and co-registered contoured and graded histology images, was used to quantify the positive predictive values and variability of observer scoring of lesions following the Prostate Imaging Reporting and Data System (PI-RADS) guidelines, the variability of target volume contouring, and appropriate expansion margins from target volumes to achieve coverage of histologically defined cancer. The analysis of lesion scoring showed that a PI-RADS overall cancer likelihood of 5, denoting “highly likely cancer”, had a positive predictive value of 85% for Gleason 7 cancer (and 93% for lesions with volumes \u3e0.5 cm3 measured on mpMRI) and that PI-RADS scores were positively correlated with histological grade (ρ=0.6). However, the analysis also showed interobserver differences in PI-RADS score of 0.6 to 1.2 (on a 5-point scale) and an agreement kappa value of only 0.30. The analysis of target volume contouring showed that target volume contours with suitable margins can achieve near-complete histological coverage for detected lesions, despite the presence of high interobserver spatial variability in target volumes. Prostate cancer imaging and delineation have the potential to support multiple stages in the management of localized prostate cancer. Targeted biopsy procedures with optimized targeting based on tumor delineation may help distinguish patients who need treatment from those who need active surveillance. Ongoing monitoring of tumor burden based on delineation in patients undergoing active surveillance may help identify those who need to progress to therapy early while the cancer is still curable. Preferentially targeting therapies at delineated target volumes may lower the morbidity associated with aggressive cancer treatment and improve outcomes in low-intermediate-risk patients. Measurements of the accuracy and variability of lesion scoring and target volume contouring on mpMRI will clarify its value in supporting these roles

Modeling Surfaces from Volume Data Using Nonparallel Contours

Magnetic resonance imaging: MRI) and computed tomography: CT) scanners have long been used to produce three-dimensional samplings of anatomy elements for use in medical visualization and analysis. From such datasets, physicians often need to construct surfaces representing anatomical shapes in order to conduct treatment, such as irradiating a tumor. Traditionally, this is done through a time-consuming and error-prone process in which an experienced scientist or physician marks a series of parallel contours that outline the structures of interest. Recent advances in surface reconstruction algorithms have led to methods for reconstructing surfaces from nonparallel contours that could greatly reduce the manual component of this process. Despite these technological advances, the segmentation process has remained unchanged. This dissertation takes the first steps toward bridging the gap between the new surface reconstruction technologies and bringing those methods to use in clinical practice. We develop VolumeViewer, a novel interface for modeling surfaces from volume data by allowing the user to sketch contours on arbitrarily oriented cross-sections of the volume. We design the algorithms necessary to support nonparallel contouring, and we evaluate the system with medical professionals using actual patient data. In this way, we begin to understand how nonparallel contouring can aid the segmentation process and expose the challenges associated with a nonparallel contouring system in practice