Noninvasive in vivo imaging technologies enable researchers and clinicians to detect the presence of disease and longitudinally study its progression. By revealing anatomical, functional, or molecular changes, imaging tools can provide a near real-time assessment of important biological events. At the preclinical research level, imaging plays an important role by allowing disease mechanisms and potential therapies to be evaluated noninvasively. Because functional and molecular changes often precede gross anatomical changes, there has been a significant amount of research exploring the ability of different imaging modalities to track these aspects of various diseases. Herein, we present a novel robotic preclinical contrast-enhanced ultrasound system and demonstrate its use in evaluating tumors in a rodent model. By leveraging recent advances in ultrasound, this system favorably compares with other modalities, as it can perform anatomical, functional, and molecular imaging and is cost-effective, portable, and high throughput, without using ionizing radiation. Furthermore, this system circumvents many of the limitations of conventional preclinical ultrasound systems, including a limited field-of-view, low throughput, and large user variability.

ITK-SNAP is an interactive software tool for manual and semi-automatic segmentation of 3D medical images. This paper summarizes major new features added to ITK-SNAP over the last decade. The main focus of the paper is on new features that support semi-automatic segmentation of multi-modality imaging datasets, such as MRI scans acquired using different contrast mechanisms (e.g., T1, T2, FLAIR). The new functionality uses decision forest classifiers trained interactively by the user to transform multiple input image volumes into a foreground/background probability map; this map is then input as the data term to the active contour evolution algorithm, which yields regularized surface representations of the segmented objects of interest. The new functionality is evaluated in the context of high-grade and low-grade glioma segmentation by three expert neuroradiogists and a non-expert on a reference dataset from the MICCAI 2013 Multi-Modal Brain Tumor Segmentation Challenge (BRATS). The accuracy of semi-automatic segmentation is competitive with the top specialized brain tumor segmentation methods evaluated in the BRATS challenge, with most results obtained in ITK-SNAP being more accurate, relative to the BRATS reference manual segmentation, than the second-best performer in the BRATS challenge; and all results being more accurate than the fourth-best performer. Segmentation time is reduced over manual segmentation by 2.5 and 5 times, depending on the rater. Additional experiments in interactive placenta segmentation in 3D fetal ultrasound illustrate the generalizability of the new functionality to a different problem domain.

We introduce a quantitative and automated method for personalized cranial shape remodeling via fronto-orbital advancement surgery. This paper builds on an objective method for automatic quantification of malformations caused by metopic craniosynostosis in children and presents a framework for personalized interventional planning. First, skull malformations are objectively quantified using a statistical atlas of normal cranial shapes. Then, we propose a method based on poly-rigid image registration that takes into account both the clinical protocol for fronto-orbital advancement and the physical constraints in the skull to plan the creation of the optimal post-surgical shape. Our automated surgical planning technique aims to minimize cranial malformations. The method was used to calculate the optimal shape for 11 infants with age 3.8±3.0 month old presenting metopic craniosynostosis and cranial malformations. The post-surgical cranial shape provided for each patient presented a significant average malformation reduction of 49% in the frontal cranial bones, and achieved shapes whose malformations were within healthy ranges. To our knowledge, this is the first work that presents an automatic framework for an objective and personalized surgical planning for craniosynostosis treatment.

This document describes a new class, itk::RLEImage, which uses run-length encoding to reduce the memory needed for storage of label maps. This class is accompanied by all the iterators to make it a dropin replacement for itk::Image. By changing the image typedef to itk::RLEImage, many ITK image processing algorithms build without modification and with minimal performance overhead. However, it is not possible if the user code uses GetBufferPointer() or otherwise assumes a linear pixel layout.This class is implemented to reduce the memory use of ITK-SNAP (www.itksnap.org), so ITKSNAP is the base for measuring the quantitative results.The class, accompanying iterator specializations, automated regression tests, and test data are all packaged as an ITK remote module https://github.com/KitwareMedical/ITKRLEImage.

This document describes a new class, itk::MorphologicalContourInterpolator, which implements a method proposed by Albu et al. in 2008. Interpolation is done by first determining correspondence between shapes on adjacent segmented slices by detecting overlaps, then aligning the corresponding shapes, generating transition sequence of one-pixel dilations and taking the median as result. Recursion is employed if the original segmented slices are separated by more than one empty slice.This class is n-dimensional, and supports inputs of 3 or more dimensions. `Slices’ are n-1-dimensional, and can be both automatically detected and manually set. The class is efficient in both memory used and execution time. It requires little memory in addition to allocation of input and output images. The implementation is multi-threaded, and processing one of the test inputs takes around 1-2 seconds on a quad-core processor.The class is tested to operate on both itk::Image and itk::RLEImage. Since all the processing is done on extracted slices, usage of itk::RLEImage for input and/or output affects performance to a limited degree.This class is implemented to ease manual segmentation in ITK-SNAP (www.itksnap.org). The class, along with test data and automated regression tests is packaged as an ITK remote module https://github.com/KitwareMedical/ITKMorphologicalContourInterpolation.

The most common sellar lesion is the pituitary adenoma, and sellar tumors are approximately 10-15% of all intracranial neoplasms. Manual slice-by-slice segmentation takes quite some time that can be reduced by using the appropriate algorithms. In this contribution, we present a segmentation method for pituitary adenoma. The method is based on an algorithm that we have applied recently to segmenting glioblastoma multiforme. A modification of this scheme is used for adenoma segmentation that is much harder to perform, due to lack of contrast-enhanced boundaries. In our experimental evaluation, neurosurgeons performed manual slice-by-slice segmentation of ten magnetic resonance imaging (MRI) cases. The segmentations were compared to the segmentation results of the proposed method using the Dice Similarity Coefficient (DSC). The average DSC for all datasets was 75.92%±7.24%. A manual segmentation took about four minutes and our algorithm required about one second.

Sensorineural hearing loss is becoming one the most common reasons of disability. Worldwide 278 million people (around 25% of people above 45 years) suffer from moderate to several hearing disorders. Cochlear implantation (CI) enables to convert sound to an electrical signal that directly stimulates the auditory nerves via the electrode array surgically placed. However, this technique is intrinsically patient-dependent and its range of outcomes is very broad. A major source of outcome variability resides in the electrode array insertion. It has been reported to be one of the most important steps in cochlear implant surgery. In this context, we propose a method for patient-specific virtual electrode insertion further used into a finite element electrical simulation, and consequently improving the planning of the surgical implantation. The anatomical parameters involved in the electrode insertion such as the curvature and the number of turns of the cochlea, make virtual insertion highly challenging. Moreover, the influence of the insertion parameters and the use of different manufactured electrode arrays increase the range of scenarios to be considered for the implantation of a given patient. To this end, the method we propose is fast, easily parameterizable and applicable to a wide range of anatomies and insertion configurations. Our method is novel for targeting automatic virtual electrode insertion. Also, it combines high-resolution imaging techniques and clinical data to be further used into a finite element study and predict implantation outcomes in humans.

We present a rectangle-based segmentation algorithm that sets up a graph and performs a graph cut to separate an object from the background. However, graph-based algorithms distribute the graph's nodes uniformly and equidistantly on the image. Then, a smoothness term is added to force the cut to prefer a particular shape. This strategy does not allow the cut to prefer a certain structure, especially when areas of the object are indistinguishable from the background. We solve this problem by referring to a rectangle shape of the object when sampling the graph nodes, i.e., the nodes are distributed non-uniformly and non-equidistantly on the image. This strategy can be useful, when areas of the object are indistinguishable from the background. For evaluation, we focus on vertebrae images from Magnetic Resonance Imaging (MRI) datasets to support the time consuming manual slice-by-slice segmentation performed by physicians. The ground truth of the vertebrae boundaries were manually extracted by two clinical experts (neurological surgeons) with several years of experience in spine surgery and afterwards compared with the automatic segmentation results of the proposed scheme yielding an average Dice Similarity Coefficient (DSC) of 90.97±2.2%.

The most common primary brain tumors are gliomas, evolving from the cerebral supportive cells. For clinical follow-up, the evaluation of the preoperative tumor volume is essential. Volumetric assessment of tumor volume with manual segmentation of its outlines is a time-consuming process that can be overcome with the help of computerized segmentation methods. In this contribution, two methods for World Health Organization (WHO) grade IV glioma segmentation in the human brain are compared using magnetic resonance imaging (MRI) patient data from the clinical routine. One method uses balloon inflation forces, and relies on detection of high intensity tumor boundaries that are coupled with the use of contrast agent gadolinium. The other method sets up a directed and weighted graph and performs a min-cut for optimal segmentation results. The ground truth of the tumor boundaries - for evaluating the methods on 27 cases - is manually extracted by neurosurgeons with several years of experience in the resection of gliomas. A comparison is performed using the Dice Similarity Coefficient (DSC), a measure for the spatial overlap of different segmentation results.