Real-Time Hand Shape Classification

The problem of hand shape classification is challenging since a hand is characterized by a large number of degrees of freedom. Numerous shape descriptors have been proposed and applied over the years to estimate and classify hand poses in reasonable time. In this paper we discuss our parallel framework for real-time hand shape classification applicable in real-time applications. We show how the number of gallery images influences the classification accuracy and execution time of the parallel algorithm. We present the speedup and efficiency analyses that prove the efficacy of the parallel implementation. Noteworthy, different methods can be used at each step of our parallel framework. Here, we combine the shape contexts with the appearance-based techniques to enhance the robustness of the algorithm and to increase the classification score. An extensive experimental study proves the superiority of the proposed approach over existing state-of-the-art methods.Comment: 11 page

GIFTed Demons: deformable image registration with local structure-preserving regularization using supervoxels for liver applications.

Deformable image registration, a key component of motion correction in medical imaging, needs to be efficient and provides plausible spatial transformations that reliably approximate biological aspects of complex human organ motion. Standard approaches, such as Demons registration, mostly use Gaussian regularization for organ motion, which, though computationally efficient, rule out their application to intrinsically more complex organ motions, such as sliding interfaces. We propose regularization of motion based on supervoxels, which provides an integrated discontinuity preserving prior for motions, such as sliding. More precisely, we replace Gaussian smoothing by fast, structure-preserving, guided filtering to provide efficient, locally adaptive regularization of the estimated displacement field. We illustrate the approach by applying it to estimate sliding motions at lung and liver interfaces on challenging four-dimensional computed tomography (CT) and dynamic contrast-enhanced magnetic resonance imaging datasets. The results show that guided filter-based regularization improves the accuracy of lung and liver motion correction as compared to Gaussian smoothing. Furthermore, our framework achieves state-of-the-art results on a publicly available CT liver dataset

Dosimetric evaluation of heterogeneity corrections for RTOG 0236: stereotactic body radiotherapy of inoperable stage I-II non-small-cell lung cancer.

PURPOSE: Using a retrospective analysis of treatment plans submitted from multiple institutions accruing patients to the Radiation Therapy Oncology Group (RTOG) 0236 non-small-cell stereotactic body radiotherapy protocol, the present study determined the dose prescription and critical structure constraints for future stereotactic body radiotherapy lung protocols that mandate density-corrected dose calculations. METHOD AND MATERIALS: A subset of 20 patients from four institutions participating in the RTOG 0236 protocol and using superposition/convolution algorithms were compared. The RTOG 0236 protocol required a prescription dose of 60 Gy delivered in three fractions to cover 95% of the planning target volume. Additional requirements were specified for target dose heterogeneity and the dose to normal tissue/structures. The protocol required each site to plan the patient\u27s treatment using unit density, and another plan with the same monitor units and applying density corrections was also submitted. These plans were compared to determine the dose differences. Two-sided, paired Student\u27s t tests were used to evaluate these differences. RESULTS: With heterogeneity corrections applied, the planning target volume receiving \u3e/=60 Gy decreased, on average, 10.1% (standard error, 2.7%) from 95% (p = .001). The maximal dose to any point \u3e/=2 cm away from the planning target volume increased from 35.2 Gy (standard error, 1.7) to 38.5 Gy (standard error, 2.2). CONCLUSION: Statistically significant dose differences were found with the heterogeneity corrections. The information provided in the present study is being used to design future heterogeneity-corrected RTOG stereotactic body radiotherapy lung protocols to match the true dose delivered for RTOG 0236

Pieces-of-parts for supervoxel segmentation with global context: Application to DCE-MRI tumour delineation

Rectal tumour segmentation in dynamic contrast-enhanced MRI (DCE-MRI) is a challenging task, and an automated and consistent method would be highly desirable to improve the modelling and prediction of patient outcomes from tissue contrast enhancement characteristics – particularly in routine clinical practice. A framework is developed to automate DCE-MRI tumour segmentation, by introducing: perfusion-supervoxels to over-segment and classify DCE-MRI volumes using the dynamic contrast enhancement characteristics; and the pieces-of-parts graphical model, which adds global (anatomic) constraints that further refine the supervoxel components that comprise the tumour. The framework was evaluated on 23 DCE-MRI scans of patients with rectal adenocarcinomas, and achieved a voxelwise area-under the receiver operating characteristic curve (AUC) of 0.97 compared to expert delineations. Creating a binary tumour segmentation, 21 of the 23 cases were segmented correctly with a median Dice similarity coefficient (DSC) of 0.63, which is close to the inter-rater variability of this challenging task. A second study is also included to demonstrate the method’s generalisability and achieved a DSC of 0.71. The framework achieves promising results for the underexplored area of rectal tumour segmentation in DCE-MRI, and the methods have potential to be applied to other DCE-MRI and supervoxel segmentation problems

Tumour subregion analysis of colorectal liver metastases using semi-automated clustering based on DCE-MRI: Comparison with histological subregions and impact on pharmacokinetic parameter analysis.

PURPOSE: To use a novel segmentation methodology based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to define tumour subregions of liver metastases from colorectal cancer (CRC), to compare these with histology, and to use these to compare extracted pharmacokinetic (PK) parameters between tumour subregions. MATERIALS AND METHODS: This ethically-approved prospective study recruited patients with CRC and ≥1 hepatic metastases scheduled for hepatic resection. Patients underwent DCE-MRI pre-metastasectomy. Histological sections of resection specimens were spatially matched to DCE-MRI acquisitions and used to define histological subregions of viable and non-viable tumour. A semi-automated voxel-wise image segmentation algorithm based on the DCE-MRI contrast-uptake curves was used to define imaging subregions of viable and non-viable tumour. Overlap of histologically-defined and imaging subregions was compared using the Dice similarity coefficient (DSC). DCE-MRI PK parameters were compared for the whole tumour and histology-defined and imaging-derived subregions. RESULTS: Fourteen patients were included in the analysis. Direct histological comparison with imaging was possible in nine patients. Mean DSC for viable tumour subregions defined by imaging and histology was 0.738 (range 0.540-0.930). There were significant differences between Ktrans and kep for viable and non-viable subregions (p < 0.001) and between whole lesions and viable subregions (p < 0.001). CONCLUSION: We demonstrate good concordance of viable tumour segmentation based on pre-operative DCE-MRI with a post-operative histological gold-standard. This can be used to extract viable tumour-specific values from quantitative image analysis, and could improve treatment response assessment in clinical practice

CyberKnife for hilar lung tumors: report of clinical response and toxicity

<p>Abstract</p> <p>Objective</p> <p>To report clinical efficacy and toxicity of fractionated CyberKnife radiosurgery for the treatment of hilar lung tumors.</p> <p>Methods</p> <p>Patients presenting with primary and metastatic hilar lung tumors, treated using the CyberKnife system with Synchrony fiducial tracking technology, were retrospectively reviewed. Hilar location was defined as abutting or invading a mainstem bronchus. Fiducial markers were implanted by conventional bronchoscopy within or adjacent to tumors to serve as targeting references. A prescribed dose of 30 to 40 Gy to the gross tumor volume (GTV) was delivered in 5 fractions. Clinical examination and PET/CT imaging were performed at 3 to 6-month follow-up intervals.</p> <p>Results</p> <p>Twenty patients were accrued over a 4 year period. Three had primary hilar lung tumors and 17 had hilar lung metastases. The median GTV was 73 cc (range 23-324 cc). The median dose to the GTV was 35 Gy (range, 30 - 40 Gy), delivered in 5 fractions over 5 to 8 days (median, 6 days). The resulting mean maximum point doses delivered to the esophagus and mainstem bronchus were 25 Gy (range, 11 - 39 Gy) and 42 Gy (range, 30 - 49 Gy), respectively. Of the 17 evaluable patients with 3 - 6 month follow-up, 4 patients had a partial response and 13 patients had stable disease. AAT t a median follow-up of 10 months, the 1-year Kaplan-Meier local control and overall survival estimates were 63% and 54%, respectively. Toxicities included one patient experiencing grade II radiation esophagitis and one patient experiencing grade III radiation pneumonitis. One patient with gross endobronchial tumor within the mainstem bronchus developed a bronchial fistula and died after receiving a maximum bronchus dose of 49 Gy.</p> <p>Conclusion</p> <p>CyberKnife radiosurgery is an effective palliative treatment option for hilar lung tumors, but local control is poor at one year. Maximum point doses to critical structures may be used as a guide for limiting toxicities. Preliminary results suggest that dose escalation alone is unlikely to enhance the therapeutic ratio of hilar lung tumors and novel approaches, such as further defining the patient population or employing the use of radiation sensitizers, should be investigated.</p

Radical stereotactic radiosurgery with real-time tumor motion tracking in the treatment of small peripheral lung tumors

<p>Abstract</p> <p>Background</p> <p>Recent developments in radiotherapeutic technology have resulted in a new approach to treating patients with localized lung cancer. We report preliminary clinical outcomes using stereotactic radiosurgery with real-time tumor motion tracking to treat small peripheral lung tumors.</p> <p>Methods</p> <p>Eligible patients were treated over a 24-month period and followed for a minimum of 6 months. Fiducials (3–5) were placed in or near tumors under CT-guidance. Non-isocentric treatment plans with 5-mm margins were generated. Patients received 45–60 Gy in 3 equal fractions delivered in less than 2 weeks. CT imaging and routine pulmonary function tests were completed at 3, 6, 12, 18, 24 and 30 months.</p> <p>Results</p> <p>Twenty-four consecutive patients were treated, 15 with stage I lung cancer and 9 with single lung metastases. Pneumothorax was a complication of fiducial placement in 7 patients, requiring tube thoracostomy in 4. All patients completed radiation treatment with minimal discomfort, few acute side effects and no procedure-related mortalities. Following treatment transient chest wall discomfort, typically lasting several weeks, developed in 7 of 11 patients with lesions within 5 mm of the pleura. Grade III pneumonitis was seen in 2 patients, one with prior conventional thoracic irradiation and the other treated with concurrent Gefitinib. A small statistically significant decline in the mean % predicted DLCO was observed at 6 and 12 months. All tumors responded to treatment at 3 months and local failure was seen in only 2 single metastases. There have been no regional lymph node recurrences. At a median follow-up of 12 months, the crude survival rate is 83%, with 3 deaths due to co-morbidities and 1 secondary to metastatic disease.</p> <p>Conclusion</p> <p>Radical stereotactic radiosurgery with real-time tumor motion tracking is a promising well-tolerated treatment option for small peripheral lung tumors.</p