Cochlear implants (CIs) use electrode arrays that are surgically inserted into the cochlea to stimulate frequency-mapped nerve endings to treat patients with hearing loss. CIs are programmed postoperatively by audiologists using behavioral tests without information on electrode–cochlea spatial relationship. We have recently developed techniques to segment the intracochlear anatomy and to localize individual contacts in clinically acquired computed tomography (CT) images. Using this information, we have proposed a programming strategy that we call image-guided CI programming (IGCIP), and we have shown that it significantly improves outcomes for both adult and pediatric recipients. One obstacle to large-scale deployment of this technique is the need for manual intervention in some processing steps. One of these is the rough registration of images prior to the use of automated intensity-based algorithms. Although seemingly simple, the heterogeneity of our image set makes this task challenging. We propose a solution that relies on the automated random forest-based localization of multiple landmarks used to estimate an initial transformation with a point-based registration method. Results show that it produces results that are equivalent to a manual initialization. This work is an important step toward the full automation of IGCIP.
KEYWORDS: Image registration, Head, Magnetic resonance imaging, Brain, 3D image processing, Medical imaging, 3D modeling, Data modeling, Image segmentation, Neuroimaging
Medical image registration establishes a correspondence between images of biological structures, and it is at the core of many applications. Commonly used deformable image registration methods depend on a good preregistration initialization. We develop a learning-based method to automatically find a set of robust landmarks in three-dimensional MR image volumes of the head. These landmarks are then used to compute a thin plate spline-based initialization transformation. The process involves two steps: (1) identifying a set of landmarks that can be reliably localized in the images and (2) selecting among them the subset that leads to a good initial transformation. To validate our method, we use it to initialize five well-established deformable registration algorithms that are subsequently used to register an atlas to MR images of the head. We compare our proposed initialization method with a standard approach that involves estimating an affine transformation with an intensity-based approach. We show that for all five registration algorithms the final registration results are statistically better when they are initialized with the method that we propose than when a standard approach is used. The technique that we propose is generic and could be used to initialize nonrigid registration algorithms for other applications.
KEYWORDS: Neuroimaging, Image registration, Head, Magnetic resonance imaging, Medical imaging, 3D image processing, Machine learning, Image segmentation, Brain, 3D modeling, Data modeling
Medical image registration establishes a correspondence between images of biological structures and it is at the core of
many applications. Commonly used deformable image registration methods are dependent on a good preregistration
initialization. The initialization can be performed by localizing homologous landmarks and calculating a point-based
transformation between the images. The selection of landmarks is however important. In this work, we present a
learning-based method to automatically find a set of robust landmarks in 3D MR image volumes of the head to initialize
non-rigid transformations. To validate our method, these selected landmarks are localized in unknown image volumes
and they are used to compute a smoothing thin-plate splines transformation that registers the atlas to the volumes. The
transformed atlas image is then used as the preregistration initialization of an intensity-based non-rigid registration
algorithm. We show that the registration accuracy of this algorithm is statistically significantly improved when using the
presented registration initialization over a standard intensity-based affine registration.
Cochlear Implants (CIs) are electrode arrays that are surgically inserted into the cochlea. Individual contacts stimulate frequency-mapped nerve endings thus replacing the natural electro-mechanical transduction mechanism. CIs are programmed post-operatively by audiologists but this is currently done using behavioral tests without imaging information that permits relating electrode position to inner ear anatomy. We have recently developed a series of image processing steps that permit the segmentation of the inner ear anatomy and the localization of individual contacts. We have proposed a new programming strategy that uses this information and we have shown in a study with 68 participants that 78% of long term recipients preferred the programming parameters determined with this new strategy. A limiting factor to the large scale evaluation and deployment of our technique is the amount of user interaction still required in some of the steps used in our sequence of image processing algorithms. One such step is the rough registration of an atlas to target volumes prior to the use of automated intensity-based algorithms when the target volumes have very different fields of view and orientations. In this paper we propose a solution to this problem. It relies on a random forest-based approach to automatically localize a series of landmarks. Our results obtained from 83 images with 132 registration tasks show that automatic initialization of an intensity-based algorithm proves to be a reliable technique to replace the manual step.
KEYWORDS: Image segmentation, Thalamus, Image registration, Magnetic resonance imaging, Image resolution, Statistical modeling, Lithium, 3D modeling, Signal to noise ratio, Surgery
Accurate and reliable identification of thalamic nuclei is important for surgical interventions and neuroanatomical studies. This is a challenging task due to their small sizes and low intra-thalamic contrast in standard T1-weighted or T2- weighted images. Previously proposed techniques rely on diffusion imaging or functional imaging. These require additional scanning and suffer from the low resolution and signal-to-noise ratio in these images. In this paper, we aim to directly segment the thalamic nuclei in standard 3T T1-weighted images using shape models. We manually delineate the structures in high-field MR images and build high resolution shape models from a group of subjects. We then investigate if the nuclei locations can be inferred from the whole thalamus. To do this, we hierarchically fit joint models. We start from the entire thalamus and fit a model that captures the relation between the thalamus and large nuclei groups. This allows us to infer the boundaries of these nuclei groups and we repeat the process until all nuclei are segmented. We validate our method in a leave-one-out fashion with seven subjects by comparing the shape-based segmentations on 3T images to the manual contours. Results we have obtained for major nuclei (dice coefficients ranging from 0.57 to 0.88 and mean surface errors from 0.29mm to 0.72mm) suggest the feasibility of using such joint shape models for localization. This may have a direct impact on surgeries such as Deep Brain Stimulation procedures that require the implantation of stimulating electrodes in specific thalamic nuclei.
Deep brain stimulation, which is used to treat various neurological disorders, involves implanting a permanent electrode into precise targets deep in the brain. Accurate pre-operative localization of the targets on pre-operative MRI sequence is challenging as these are typically located in homogenous regions with poor contrast. Population-based statistical atlases can assist with this process. Such atlases are created by acquiring the location of efficacious regions from numerous subjects and projecting them onto a common reference image volume using some normalization method. In previous work, we presented results concluding that non-rigid registration provided the best result for such normalization. However, this process could be biased by the choice of the reference image and/or registration approach. In this paper, we have qualitatively and quantitatively compared the performance of six recognized deformable registration methods at normalizing such data in poor contrasted regions onto three different reference volumes using a unique set of data from 100 patients. We study various metrics designed to measure the centroid, spread, and shape of the normalized data. This study leads to a total of 1800 deformable registrations and results show that statistical atlases constructed using different deformable registration methods share comparable centroids and spreads with marginal differences in their shape. Among the six methods being studied, Diffeomorphic Demons produces the largest spreads and centroids that are the furthest apart from the others in general. Among the three atlases, one atlas consistently outperforms the other two with smaller spreads for each algorithm. However, none of the differences in the spreads were found to be statistically significant, across different algorithms or across different atlases.
In deep brain stimulation surgeries, stimulating electrodes are placed at specific targets in the deep brain to treat
neurological disorders. Reaching these targets safely requires avoiding critical structures in the brain. Meticulous
planning is required to find a safe path from the cortical surface to the intended target. Choosing a trajectory
automatically is difficult because there is little consensus among neurosurgeons on what is optimal. Our goals are to
design a path planning system that is able to learn the preferences of individual surgeons and, eventually, to standardize
the surgical approach using this learned information. In this work, we take the first step towards these goals, which is to
develop a trajectory planning approach that is able to effectively mimic individual surgeons and is designed such that
parameters, which potentially can be automatically learned, are used to describe an individual surgeon's preferences. To
validate the approach, two neurosurgeons were asked to choose between their manual and a computed trajectory, blinded
to their identity. The results of this experiment showed that the neurosurgeons preferred the computed trajectory over
their own in 10 out of 40 cases. The computed trajectory was judged to be equivalent to the manual one or otherwise
acceptable in 27 of the remaining cases. These results demonstrate the potential clinical utility of computer-assisted path
planning.
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