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 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 diagnosis of certain spine pathologies, such as scoliosis, spondylolisthesis and vertebral fractures, is part of the daily clinical routine. Very frequently, magnetic resonance image data are used to diagnose these kinds of pathologies in order to avoid exposing patients to harmful radiation, like X-ray. We present a method which detects and segments all acquired vertebral bodies, with minimal user intervention. This allows an automatic diagnosis to detect scoliosis, spondylolisthesis and crushed vertebrae. Our approach consists of three major steps. First, vertebral centres are detected using a Viola-Jones like method, and then the vertebrae are segmented in a parallel manner, and finally, geometric diagnostic features are deduced in order to diagnose the three diseases. Our method was evaluated on 26 lumbar datasets containing 234 reference vertebrae. Vertebra detection has 7.1% false negatives and 1.3% false positives. The average Dice coefficient to manual reference is 79.3% and mean distance error is 1.76 mm. No severe case of the three illnesses was missed, and false alarms occurred rarely-0% for scoliosis, 3.9% for spondylolisthesis and 2.6% for vertebral fractures. The main advantages of our method are high speed, robust handling of a large variety of routine clinical images, and simple and minimal user interaction.
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.
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.
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