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.

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%.

Segmentation of vertebral bodies is useful for diagnosis of certain spine pathologies, such as scoliosis, spondylolisthesis and vertebral fractures. In this paper, we present a fast and semi-automatic approach for spine segmentation in routine clinical MR images. Segmenting a single vertebra is based on multiple-feature boundary classification and mesh inflation, and starts with a simple point-in-vertebra initialization. The inflation retains a star-shape geometry to prevent selfintersections and uses a constrained subdivision hierarchy to control smoothness. Analyzing the shape of the first vertebra, the main spine direction is deduced and the locations of neighboring vertebral bodies are estimated for further segmentation. The method was tested on 11 routine lumbar datasets with 92 reference vertebrae resulting in a detection rate of 93%. The average Dice Similarity Coefficient (DSC) against manual reference segmentations was 78%, which is on par with state of the art. The main advantages of our method are high speed and a low amount of user interaction.

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.

Gliomas are the most common primary brain tumors, 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 computer-assisted segmentation methods. In this paper, a semi-automatic approach for World Health Organization (WHO) grade IV glioma segmentation is introduced that uses balloon inflation forces, and relies on the detection of high-intensity tumor boundaries that are coupled by using contrast agent gadolinium. The presented method is evaluated on 27 magnetic resonance imaging (MRI) data sets and the ground truth data of the tumor boundaries - for evaluation of the results - are manually extracted by neurosurgeons.

Many state-of-the art visualization techniques must be tailored to the specific type of dataset, its modality (CT, MRI, etc.), the recorded object or anatomical region (head, spine, abdomen, etc.) and other parameters related to the data acquisition process. While parts of the information (imaging modality and acquisition sequence) may be obtained from the meta-data stored with the volume scan, there is important information which is not stored explicitly (anatomical region, tracing compound). Also, meta-data might be incomplete, inappropriate or simply missing. This paper presents a novel and simple method of determining the type of dataset from previously defined categories. 2D histograms based on intensity and gradient magnitude of datasets are used as input to a neural network, which classifies it into one of several categories it was trained with. The proposed method is an important building block for visualization systems to be used autonomously by non-experts. The method has been tested on 80 datasets, divided into 3 classes and a "rest" class. A significant result is the ability of the system to classify datasets into a specific class after being trained with only one dataset of that class. Other advantages of the method are its easy implementation and its high computational performance.

For medical volume visualization, one of the most important tasks is to reveal clinically relevant details from the 3D scan (CT, MRI ...), e.g. the coronary arteries, without obscuring them with less significant parts. These volume datasets contain different materials which are difficult to extract and visualize with 1D transfer functions based solely on the attenuation coefficient. Multi-dimensional transfer functions allow a much more precise classification of data which makes it easier to separate different surfaces from each other. Unfortunately, setting up multi-dimensional transfer functions can become a fairly complex task, generally accomplished by trial and error. This paper explains neural networks, and then presents an efficient way to speed up visualization process by semi-automatic transfer function generation. We describe how to use neural networks to detect distinctive features shown in the 2D histogram of the volume data and how to use this information for data classification.