In low-dose prostate brachytherapy treatment, a large number of radioactive seeds is implanted in and adjacent to the prostate gland. Planning of this treatment involves the determination of a Planning Target Volume (PTV), followed by defining the optimal number of seeds, needles and their coordinates for implantation. The two major planning tasks, i.e. PTV determination and seed definition, are associated with inter- and intra-expert variability. Moreover, since these two steps are performed in sequence, the variability is accumulated in the overall treatment plan. In this paper, we introduce a model based on a data fusion technique that enables joint determination of PTV and the minimum Prescribed Isodose (mPD) map. The model captures the correlation between different information modalities consisting of transrectal ultrasound (TRUS) volumes, PTV and isodose contours. We take advantage of joint Independent Component Analysis (jICA) as a linear decomposition technique to obtain a set of joint components that optimally describe such correlation. We perform a component stability analysis to generate a model with stable parameters that predicts the PTV and isodose contours solely based on a new patient TRUS volume. We propose a framework for both modeling and prediction processes and evaluate it on a dataset of 60 brachytherapy treatment records. We show PTV prediction error of 10:02±4:5% and the V100 isodose overlap of 97±3:55% with respect to the clinical gold standard.
We propose a joint Source-Based Analysis (jSBA) framework to identify brain structural variations in patients with Major Depressive Disorder (MDD). In this framework, features representing position, orientation and size (i.e. pose), shape, and local tissue composition are extracted. Subsequently, simultaneous analysis of these features within a joint analysis method is performed to generate the basis sources that show signi cant di erences between subjects with MDD and those in healthy control. Moreover, in a cross-validation leave- one-out experiment, we use a Fisher Linear Discriminant (FLD) classi er to identify individuals within the MDD group. Results show that we can classify the MDD subjects with an accuracy of 76% solely based on the information gathered from the joint analysis of pose, shape, and tissue composition in multiple brain structures.
Brachytherapy as one of the treatment methods for prostate cancer takes place by implantation of radioactive seeds inside the gland. The standard of care for this treatment procedure is to acquire transrectal ultrasound images of the prostate which are segmented in order to plan the appropriate seed placement. The segmentation process is usually performed either manually or semi-automatically and is associated with subjective errors because the prostate visibility is limited in ultrasound images. The current segmentation process also limits the possibility of intra-operative delineation of the prostate to perform real-time dosimetry. In this paper, we propose a computationally inexpensive and fully automatic segmentation approach that takes advantage of previously segmented images to form a joint space of images and their segmentations. We utilize joint Independent Component Analysis method to generate a model which is further employed to produce a probability map of the target segmentation. We evaluate this approach on the transrectal ultrasound volume images of 60 patients using a leave-one-out cross-validation approach. The results are compared with the manually segmented prostate contours that were used by clinicians to plan brachytherapy procedures. We show that the proposed approach is fast with comparable accuracy and precision to those found in previous studies on TRUS segmentation.
Shape deformations and volumetric changes in the hippocampus and amygdala have previously been noted in Major
Depressive Disorder (MDD). Unfortunately, these analyses are limited because relative shape and pose (rigid+scale
transformation) information of multiple objects in brain are generally disregarded. We hypothesize that this information
might complement studies of limbic structural deformation in MDD. We focus on changes in temporal (e.g., superior,
middle and inferior temporal gyrus) and limbic (e.g., hippocampus and amygdala) lobes. Here, we use a multi-object
statistical pose and shape model to analyze imaging data from young people with and without a depressive disorder.
Nineteen individuals with a depressive disorder (mean age: 17.85) and twenty six healthy controls (age: 18) were
enrolled in the study. A segmented atlas in MNI space has been used to segment hippocampus, amygdala,
parahippocampal gyri, putamen, and the superior, inferior and middle temporal gyri in both hemispheres of the brain.
Points on the surface of each structure were extracted and warped to each subjects’ structural MRI. These corresponding
surface points were used within the analysis, to extract the pose and shape features. Pose and shape differences were
detected between the two groups, such that second principal mode of pose variation (p = 0.022), and first principal mode
of shape variation (p = 0.049) were found to differ significantly between the two groups.