Major depressive disorder (MDD) has previously been linked to structural changes in several brain regions, particularly in the medial temporal lobes (Bellani, Baiano, Brambilla, 2010; Bellani, Baiano, Brambilla, 2011). This has been determined using voxel-based morphometry, segmentation algorithms, and analysis of shape deformations (Bell-McGinty et al., 2002; Bergouignan et al., 2009; Posener et al., 2003; Vasic et al., 2008; Zhao et al., 2008): these are methods in which information related to the shape and the pose (the size, and anatomical position and orientation) of structures is lost. Here, we incorporate information about shape and pose to measure structural deformation in adolescents and young adults with and without depression (as measured using the Beck Depression Inventory and Diagnostic and Statistical Manual of Mental Disorders criteria). As a hypothesis-generating study, a significance level of p < 0.05, uncorrected for multiple comparisons, was used, so that subtle morphological differences in brain structures between adolescent depressed individuals and control participants could be identified. We focus on changes in cortical and subcortical temporal structures, and use a multi-object statistical pose and shape model to analyze imaging data from 16 females (aged 16–21) and 3 males (aged 18) with early-onset MDD, and 25 female and 1 male normal control participants, drawn from the same age range. The hippocampus, parahippocampal gyrus, putamen, and superior, inferior and middle temporal gyri in both hemispheres of the brain were automatically segmented using the LONI Probabilistic Brain Atlas (Shattuck et al., 2008) in MNI space. Points on the surface of each structure in the atlas were extracted and warped to each participant's structural MRI. These surface points were analyzed to extract the pose and shape features. Pose differences were detected between the two groups, particularly in the left and right putamina, right hippocampus, and left and right inferior temporal gyri. Shape differences were detected between the two groups, particularly in the left hippocampus and in the left and right parahippocampal gyri. Furthermore, pose measures were significantly correlated with BDI score across the whole (clinical and control) sample. Since the clinical participants were experiencing their very first episodes of MDD, morphological alteration in the medial temporal lobe appears to be an early sign of MDD, and is unlikely to result from treatment with antidepressants. Pose and shape measures of morphology, which are not usually analyzed in neuromorphometric studies, appear to be sensitive to depressive symptomatology.

Low-dose-rate prostate brachytherapy treatment takes place by implantation of small radioactive seeds in and sometimes adjacent to the prostate gland. A patient specific target anatomy for seed placement is usually determined by contouring a set of collected transrectal ultrasound images prior to implantation. Standard-of-care in prostate brachytherapy is to delineate the clinical target anatomy, which closely follows the real prostate boundary. Subsequently, the boundary is dilated with respect to the clinical guidelines to determine a planning target volume. Manual contouring of these two anatomical targets is a tedious task with relatively high observer variability. In this work, we aim to reduce the segmentation variability and planning time by proposing an efficient learning-based multi-label segmentation algorithm. We incorporate a sparse representation approach in our methodology to learn a dictionary of sparse joint elements consisting of images, and clinical and planning target volume segmentation. The generated dictionary inherently captures the relationships among elements, which also incorporates the institutional clinical guidelines. The proposed multi-label segmentation method is evaluated on a dataset of 590 brachytherapy treatment records by 5-fold cross validation. We show clinically acceptable instantaneous segmentation results for both target volumes.

A single-task functional magnetic resonance imaging (fMRI) experiment may only partially highlight alterations to functional brain networks affected by a particular disorder. Multivariate analysis across multiple fMRI tasks may increase the sensitivity of fMRI-based diagnosis. Prior research using multi-task analysis in fMRI, such as those that use joint independent component analysis (jICA), has mainly assumed that brain activity patterns evoked by different tasks are independent. This may not be valid in practice. Here, we use sparsity, which is a natural characteristic of fMRI data in the spatial domain, and propose a joint sparse representation analysis (jSRA) method to identify common information across different functional subtraction (contrast) images in data from a multi-task fMRI experiment. Sparse representation methods do not require independence, or that the brain activity patterns be nonoverlapping. We use functional subtraction images within the joint sparse representation analysis to generate joint activation sources and their corresponding sparse modulation profiles. We evaluate the use of sparse representation analysis to capture individual differences with simulated fMRI data and with experimental fMRI data. The experimental fMRI data was acquired from 16 young (age: 19-26) and 16 older (age: 57-73) adults obtained from multiple speech comprehension tasks within subjects, where an independent measure (namely, age in years) can be used to differentiate between groups. Simulation results show that this method yields greater sensitivity, precision, and higher Jaccard indexes (which measures similarity and diversity of the true and estimated brain activation sources) than does the jICA method. Moreover, superiority of the jSRA method in capturing individual differences was successfully demonstrated using experimental fMRI data.

Robot-assisted laparoscopic radical prostatectomy (RALRP) using the da Vinci surgical system is the current state-of-the-art treatment option for clinically confined prostate cancer. Given the limited field of view of the surgical site in RALRP, several groups have proposed the integration of transrectal ultrasound (TRUS) imaging in the surgical workflow to assist with accurate resection of the prostate and the sparing of the neurovascular bundles (NVBs). We previously introduced a robotic TRUS manipulator and a method for automatically tracking da Vinci surgical instruments with the TRUS imaging plane, in order to facilitate the integration of intraoperative TRUS in RALRP. Rapid and automatic registration of the kinematic frames of the da Vinci surgical system and the robotic TRUS probe manipulator is a critical component of the instrument tracking system. In this paper, we propose a fully automatic registration technique based on automatic 3-D TRUS localization of robot instrument tips pressed against the air-tissue boundary anterior to the prostate. The detection approach uses a multiscale filtering technique to identify and localize surgical instrument tips in the TRUS volume, and could also be used to detect other surface fiducials in 3-D ultrasound. Experiments have been performed using a tissue phantom and two ex vivo tissue samples to show the feasibility of the proposed methods. Also, an initial in vivo evaluation of the system has been carried out on a live anaesthetized dog with a da Vinci Si surgical system and a target registration error (defined as the root mean square distance of corresponding points after registration) of 2.68 mm has been achieved. Results show this method's accuracy and consistency for automatic registration of TRUS images to the da Vinci surgical system.

No abstract