Development of an Atlas-Based Segmentation of Cranial Nerves Using Shape-Aware Discrete Deformable Models for Neurosurgical Planning and Simulation

Twelve pairs of cranial nerves arise from the brain or brainstem and control our sensory functions such as vision, hearing, smell and taste as well as several motor functions to the head and neck including facial expressions and eye movement. Often, these cranial nerves are difficult to detect in MRI data, and thus represent problems in neurosurgery planning and simulation, due to their thin anatomical structure, in the face of low imaging resolution as well as image artifacts. As a result, they may be at risk in neurosurgical procedures around the skull base, which might have dire consequences such as the loss of eyesight or hearing and facial paralysis. Consequently, it is of great importance to clearly delineate cranial nerves in medical images for avoidance in the planning of neurosurgical procedures and for targeting in the treatment of cranial nerve disorders. In this research, we propose to develop a digital atlas methodology that will be used to segment the cranial nerves from patient image data. The atlas will be created from high-resolution MRI data based on a discrete deformable contour model called 1-Simplex mesh. Each of the cranial nerves will be modeled using its centerline and radius information where the centerline is estimated in a semi-automatic approach by finding a shortest path between two user-defined end points. The cranial nerve atlas is then made more robust by integrating a Statistical Shape Model so that the atlas can identify and segment nerves from images characterized by artifacts or low resolution. To the best of our knowledge, no such digital atlas methodology exists for segmenting nerves cranial nerves from MRI data. Therefore, our proposed system has important benefits to the neurosurgical community

Mitigating anatomical changes in head and neck radiotherapy

This thesis aims to address the issue of anatomical changes during the treatment of head and neck cancer patients. Intensity-modulated proton therapy (IMPT) shows advantages in delivering a conformal dose to the target while minimizing the dose to the adjacent normal tissue. However, in the delivery of IMPT, patients not being static can lead to dosimetric discrepancies. This thesis explores different techniques to reduce the influence of uncertainty from anatomical changes (anatomical uncertainty). In Chapter 3, anatomical variations are incorporated to reveal the robustness of a plan, thus improving the selection of a robust plan. The benefit is demonstrated in all beam arrangements used in the study. In Chapter 4, a probability model is developed to simulate major anatomical deformations at each weekly time point based on population data. For overall anatomical uncertainty prediction during treatment, the PM reduces anatomical uncertainty from 3.72±0.46 mm (no model) to 0.81±0.56 mm on average. In Chapter 5, an individual model is developed to refine the systematic prediction of population data using individual progressive information. Compared with no model, whose average anatomical uncertainty and gamma index are 4.47±1.23 mm and 93.87±0.83% at week 6, respectively, the refined individual model reduces anatomical uncertainty to 1.89±1.23 mm and improves the gamma index to 96.16±1.84% at week 6. In Chapter 6, a proposed flexible strategy creates upfront predicted replans on the predicted individual geometries from the RIM. This application of the RIM reduces the parotid Dmean by 0.53 Gy on average, achieving the minimum benefit of non-delay treatment, while the dosimetric of other organs has no significant difference with the standard replanning technique (p>0.05). This prediction-based replanning improves clinical workflow efficiency. This research integrates computational methods into IMPT treatment for head and neck patients. New perspectives for mitigating anatomical uncertainty are provided and discussed in this thesis

Deformable Contour Models for Digitizing a Printed Brainstem Atlas

The brainstem is a part of the brain that is connected to the cerebrum and the spinal cord. Ten out of twelve pairs of cranial nerves emerge from the brainstem. The cranial nerves transmit information between the brain and various parts of the body. Due to its anatomical and physiological relevance, a descriptive digital brainstem is important for neurosurgery planning and simulation. For both of these neurosurgical applications, the complexity of the brainstem requires a digital atlas approach to segmentation that maps intensities to tissues rather than less descriptive voxel or surface-based approaches. However, a descriptive brainstem atlas with adequate details for neurosurgery planning and simulation has not been developed to date. Fortunately, various textbooks contain 2D representations of the brainstem at various longitudinal coordinates. The aim of this thesis is to describe a minimally supervised method to segment sketches coinciding with slices of the brainstem featuring labeled contours. This thesis also describes a deformable contour model approach, emphasizing a 1-simplex framework, to reconstruct a 3D volume from 2D slices

Deep-Learning-Based Dose Predictor for Glioblastoma–Assessing the Sensitivity and Robustness for Dose Awareness in Contouring

External beam radiation therapy requires a sophisticated and laborious planning procedure. To improve the efficiency and quality of this procedure, machine-learning models that predict these dose distributions were introduced. The most recent dose prediction models are based on deep-learning architectures called 3D U-Nets that give good approximations of the dose in 3D almost instantly. Our purpose was to train such a 3D dose prediction model for glioblastoma VMAT treatment and test its robustness and sensitivity for the purpose of quality assurance of automatic contouring. From a cohort of 125 glioblastoma (GBM) patients, VMAT plans were created according to a clinical protocol. The initial model was trained on a cascaded 3D U-Net. A total of 60 cases were used for training, 15 for validation and 20 for testing. The prediction model was tested for sensitivity to dose changes when subject to realistic contour variations. Additionally, the model was tested for robustness by exposing it to a worst-case test set containing out-of-distribution cases. The initially trained prediction model had a dose score of 0.94 Gy and a mean DVH (dose volume histograms) score for all structures of 1.95 Gy. In terms of sensitivity, the model was able to predict the dose changes that occurred due to the contour variations with a mean error of 1.38 Gy. We obtained a 3D VMAT dose prediction model for GBM with limited data, providing good sensitivity to realistic contour variations. We tested and improved the model’s robustness by targeted updates to the training set, making it a useful technique for introducing dose awareness in the contouring evaluation and quality assurance process

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

Active Contour Based Segmentation Techniques for Medical Image Analysis

Image processing is a technique which is used to derive information from the images. Segmentation is a section of image processing for the separation or segregation of information from the required target region of the image. There are different techniques used for segmentation of pixels of interest from the image. Active contour is one of the active models in segmentation techniques, which makes use of the energy constraints and forces in the image for separation of region of interest. Active contour defines a separate boundary or curvature for the regions of target object for segmentation. The contour depends on various constraints based on which they are classified into different types such as gradient vector flow, balloon and geometric models. Active contour models are used in various image processing applications specifically in medical image processing. In medical imaging, active contours are used in segmentation of regions from different medical images such as brain CT images, MRI images of different organs, cardiac images and different images of regions in the human body. Active contours can also be used in motion tracking and stereo tracking. Thus, the active contour segmentation is used for the separation of pixels of interest for different image processing

Doctor of Philosophy

dissertationThe nervous system is comprised of an estimated 100 billion individual neurons, which are connected to one another to form a network that senses environmental stimuli and coordinates the organism's behavior. Because of the complexity of the nervous system, deciphering the developmental processes and adult wiring diagram has proved challenging. A number of axon guidance molecules have been identified; however, the means by which they guide billions of axons to their target cells in vivo remains poorly understood. Several axon guidance molecules have been found to be bifunctional, meaning they can elicit different growth cone responses depending on the presence or absence of other molecules, such as growth cone receptors, intracellular signal transduction molecules, or extracellular modulators. Axon sorting within axon tracts is perhaps a means by which axons are presorted to make a precise connection on their target cells. The zebrafish, Danio rerio, is an ideal model organism to study vertebrate axon guidance and axon sorting due to its external fertilization, optical transparency, amenability to forward genetics, and ease of making transgenic lines. In order to study axon guidance within the zebrafish retinotectal system, I developed a new method of misexpressing genes. Local misexpression can be induced by using a modified soldering iron in transgenic zebrafish in which a gene of interest is driven by a heat shock promoter. This method allowed me to examine the mechanisms by which Slit1a and Slit2 guide axons from the retina to the optic tectum. I determined the expression pattern of Slits in the zebrafish and used antisense morpholino technology to knock down Slit1a. The iv resultant axon guidance errors indicated that Slit1a acts to guide retinal axons through the optic tract. I then misexpressed Slit1a and Slit2 near the optic tract to observe their effect on axons. I found that both proteins appeared to attract retinal axons. Additionally, I saw that Slit2 seems to attract retinal axons earlier in the retinotectal pathway, at the optic chiasm. I also report on a new method, to whose development I contributed, for automated tracking of axons through electron microscopy datasets. Taken together, my results add new methods to the endeavor of mapping neural connectivity and development, and suggest a new role for Slits in axon guidance