Dice Similarity Index Research Articles

Inter-observer variability of clinical target volume delineation in radiotherapy treatment of pancreatic cancer: a multi-institutional contouring experience.

BackgroundAn observational multi-institutional study has been conducted aimed to evaluate the inter-observer variability in clinical target volume (CTV) delineation among different radiation oncologists in radiotherapy treatment of pancreatic cancer.MethodsA multi-institutional contouring dummy-run of two different cases of pancreatic cancer treated by postoperative and preoperative radiotherapy (RT) was performed. Clinical history, diagnostics, and planning CT imaging were available on AIRO website (http://www.radioterapiaitalia.it). Participants were requested to delineate CTVs according to their skills and knowledge. Aiming to quantify interobserver variability of CTVs delineations, the total volume, craniocaudal, laterolateral, and anteroposterior diameters were calculated. Descriptive statistic was calculated. The 95% Confidence Interval (95% CI) for coefficient of variation (CV) was estimated. The Dice Similarity Index (DSI) was used to evaluate the spatial overlap accuracy of the different CTVs compared with the CTVs of a national reference Centre considered as a benchmark. The mean DSI (mDSI) was calculated and reported.ResultsA total of 18 radiation oncologists from different Institutes submitted the targets. Less variability was observed for the Elective CTV rather than the Boost CTV, in both cases. The estimated CV were 28.8% (95% CI: 21.2 - 45.0%) and 20.0% (95% CI: 14.9 - 30.6%) for the Elective CTV, in adjuvant (Case 1) and neoadjuvant (Case 2) case, respectively. The mDSI value was 0.68 for the Elective CTVs in both cases (range 0.19 - 0.79 in postoperative vs range 0.35 - 0.79 in preoperative case). The mDSI was increased to 0.71 (Case 1) and 0.72 (Case 2) if the observers with a worse agreement have been excluded. On the other hand, a CV of 42.4% (95% CI: 30.1 - 72.4%) and 63.8% (95% CI: 43.9 - 119.2%) with a mDSI value of 0.44 and 0.52, were calculated for the Boost CTV in Case 1 and Case 2, respectively.ConclusionsThe CV and mDSI obtained values for Elective CTVs showed an acceptable agreement among participants either in postoperative as well in preoperative setting. Additional strategies to reduce the variability in Boost CTV delineation need to be found and promoted.

Evaluation of an Atlas-Based Segmentation Method for High-Risk Prostate Cancer With RTOG-Defined Pelvic Lymph Node Levels

Intensity modulated radiation therapy (IMRT) for high risk prostate cancer requires careful contouring and segmentation of at risk pelvic lymph nodes. However, this process can be very time consuming and difficult compared to traditional prostate only fields. One possible solution to improve target volume contouring efficiency is to use atlas based segmentation software. For this pilot study, we compiled a prostate target atlas database and sought to develop an atlas based segmentation method. Institutional review board approval was obtained to retrospectively analyze patient CT simulation scans to develop an atlas database. Atlas contours included prostate, seminal vesicles, and RTOG-defined pelvic lymph node levels for treatment volumes. Lymph node levels were contoured using RTOG guidelines by following the internal/external iliac vessels and its associated lymph nodes from the L5-S1 vertebral level to obturator nodes at the pubic symphysis. A total of 23 patients with contours reviewed by three different physicians were included in our atlas database. Once this database was compiled we tested the feasibility of developing an atlas based segmentation algorithm and tested it using Dice similarity index (DICE) scores. Atlas segmentation was designed using a leave-one-out analysis (subject being tested was excluded from the atlas). Then the 5 best-matched atlas subjects were automatically chosen and deformed to the test subject. The atlas segmentation method included an additional automatic registration approximation component to influence the intensity-based deformable registration. The 5 sets of auto contours were combined using majority vote where 3 of 5 contours overlapped. Final auto contours were then compared to the manually defined contours using DICE. Using this algorithm for all 23 atlas subjects, the mean DICE score was 0.67 +/- 0.14 for the prostate target volume and 0.74 +/- 0.05 for the pelvic lymph node target volume. Our atlas segmentation method provided accurate contours for high risk prostate cancer. This segmentation method has may have potential for greater accuracy and less variance than traditional inter-individual user contours. In particular, the accuracy of our atlas based segmentation algorithm for pelvic lymph nodes was very good. This is likely due to their association with rigid structures and reduced motion compared to other structures such as the prostate and seminal vesicles. Therefore, our results demonstrate, that atlas based auto-contouring of the pelvic nodes could both help increase consistency for treatment volumes in high risk patients between different cancer centers as well as decrease time related to treatment planning for radiation oncologist.

Semi-automatic volumetric segmentation of the upper airways in patients with pierre robin sequence.

Pierre Robin malformation is a rare craniofacial dysmorphism whose pathogenesis is multifactorial. Although there is some agreement in non-invasive treatment in less severe cases, the dispute is still open on cases with severe respiratory impairment. We present a semi-automatic novel diagnostic tool for calculating upper airway volume, in order to eventually address surgery in patients with Pierre Robin Sequence (PRS). Multidetector CT datasets of two patients and two controls were tested to assess the proposed method for ROI segmentation, upper airway volume computation and three-dimensional reconstructions. The experimental results show an irregular pattern and a severely reduced cross-sectional area (CSA) with a mean value of 8.3808 mm(2) in patients with PRS and a mean CSA value of 33.7692 mm(2) in controls (a ΔCSA of about -75%). Moreover, the similarity indexes and sensitivity/specificity values obtained showed a good segmentation performance. In particular, mean values of Jaccard and Dice similarity indexes were 91.69% and 94.07%, respectively, while the mean values of specificity and sensitivity were 96.69% and 98.03%, respectively. The proposed tool represents an easy way to perform a quantitative analysis of airway volume and useful 3D reconstructions.

Automated voxel-by-voxel tissue classification for hippocampal segmentation: Methods and validation

The hippocampus is an important structural biomarker for Alzheimer's disease (AD) and has a primary role in the pathogenesis of other neurological and psychiatric diseases. This study presents a fully automated pattern recognition system for an accurate and reproducible segmentation of the hippocampus in structural Magnetic Resonance Imaging (MRI). The method was validated on a mixed cohort of 56 T1-weighted structural brain images, and consists of three processing levels: (a) Linear registration: all brain images were registered to a standard template and an automated method was applied to capture the global shape of the hippocampus. (b) Feature extraction: all voxels included in the previously selected volume were characterized by 315 features computed from local information. (c) Voxel classification: a Random Forest algorithm was used to classify voxels as belonging or not belonging to the hippocampus. In order to improve the classification performance, an adaptive learning method based on the use of the Pearson's correlation coefficient was developed. The segmentation results (Dice similarity index = 0.81 ± 0.03) compare well with other state-of-the art approaches. A validation study was conducted on an independent dataset of 100 T1-weighted brain images, achieving significantly better results than those obtained with FreeSurfer.

Automated segmentation and analysis of normal and osteoarthritic knee menisci from magnetic resonance images – data from the Osteoarthritis Initiative

To validate an automatic scheme for the segmentation and quantitative analysis of the medial meniscus (MM) and lateral meniscus (LM) in magnetic resonance (MR) images of the knee. We analysed sagittal water-excited double-echo steady-state MR images of the knee from a subset of the Osteoarthritis Initiative (OAI) cohort. The MM and LM were automatically segmented in the MR images based on a deformable model approach. Quantitative parameters including volume, subluxation and tibial-coverage were automatically calculated for comparison (Wilcoxon tests) between knees with variable radiographic osteoarthritis (rOA), medial and lateral joint space narrowing (mJSN, lJSN) and pain. Automatic segmentations and estimated parameters were evaluated for accuracy using manual delineations of the menisci in 88 pathological knee MR examinations at baseline and 12 months time-points. The median (95% confidence-interval (CI)) Dice similarity index (DSI) (2 ∗|Auto ∩ Manual|/(|Auto|+|Manual|)∗ 100) between manual and automated segmentations for the MM and LM volumes were 78.3% (75.0-78.7), 83.9% (82.1-83.9) at baseline and 75.3% (72.8-76.9), 83.0% (81.6-83.5) at 12 months. Pearson coefficients between automatic and manual segmentation parameters ranged from r = 0.70 to r = 0.92. MM in rOA/mJSN knees had significantly greater subluxation and smaller tibial-coverage than no-rOA/no-mJSN knees. LM in rOA knees had significantly greater volumes and tibial-coverage than no-rOA knees. Our automated method successfully segmented the menisci in normal and osteoarthritic knee MR images and detected meaningful morphological differences with respect to rOA and joint space narrowing (JSN). Our approach will facilitate analyses of the menisci in prospective MR cohorts such as the OAI for investigations into pathophysiological changes occurring in early osteoarthritis (OA) development.

SU-C-9A-01: Parameter Optimization in Adaptive Region-Growing for Tumor Segmentation in PET

Purpose: To design a reliable method to determine the optimal parameter in the adaptive region-growing (ARG) algorithm for tumor segmentation in PET. Methods: The ARG uses an adaptive similarity criterion m - fσ ≤ I_PET ≤ m + fσ, so that a neighboring voxel is appended to the region based on its similarity to the current region. When increasing the relaxing factor f (f ≥ 0), the resulting volumes monotonically increased with a sharp increase when the region just grew into the background. The optimal f that separates the tumor from the background is defined as the first point with the local maximum curvature on an Error function fitted to the f-volume curve. The ARG was tested on a tumor segmentation Benchmark that includes ten lung cancer patients with 3D pathologic tumor volume as ground truth. For comparison, the widely used 42% and 50% SUVmax thresholding, Otsu optimal thresholding, Active Contours (AC), Geodesic Active Contours (GAC), and Graph Cuts (GC) methods were tested. The dice similarity index (DSI), volume error (VE), and maximum axis length error (MALE) were calculated to evaluate the segmentation accuracy. Results: The ARG provided the highest accuracy among all tested methods. Specifically, the ARG has an average DSI, VE, and MALE of 0.71, 0.29, and 0.16, respectively, better than the absolute 42% thresholding (DSI=0.67, VE= 0.57, and MALE=0.23), the relative 42% thresholding (DSI=0.62, VE= 0.41, and MALE=0.23), the absolute 50% thresholding (DSI=0.62, VE=0.48, and MALE=0.21), the relative 50% thresholding (DSI=0.48, VE=0.54, and MALE=0.26), OTSU (DSI=0.44, VE=0.63, and MALE=0.30), AC (DSI=0.46, VE= 0.85, and MALE=0.47), GAC (DSI=0.40, VE= 0.85, and MALE=0.46) and GC (DSI=0.66, VE= 0.54, and MALE=0.21) methods. Conclusions: The results suggest that the proposed method reliably identified the optimal relaxing factor in ARG for tumor segmentation in PET. This work was supported in part by National Cancer Institute Grant R01 CA172638; The dataset is provided by AAPM TG211.

SU-C-9A-03: Simultaneous Deconvolution and Segmentation for PET Tumor Delineation Using a Variational Method

Purpose: To implement a new method that integrates deconvolution with segmentation under the variational framework for PET tumor delineation. Methods: Deconvolution and segmentation are both challenging problems in image processing. The partial volume effect (PVE) makes tumor boundaries in PET image blurred which affects the accuracy of tumor segmentation. Deconvolution aims to obtain a PVE-free image, which can help to improve the segmentation accuracy. Conversely, a correct localization of the object boundaries is helpful to estimate the blur kernel, and thus assist in the deconvolution. In this study, we proposed to solve the two problems simultaneously using a variaional method so that they can benefit each other. The energy functional consists of a fidelity term and a regularization term, and the blur kernel was limited to be the isotropic Gaussian kernel. We minimized the energy functional by solving the associated Euler-Lagrange equations and taking the derivative with respect to the parameters of the kernel function. An alternate minimization method was used to iterate between segmentation, deconvolution and blur-kernel recovery. The performance of the proposed method was tested on clinic PET images of patients with non-Hodgkin's lymphoma, and compared with seven other segmentation methods using the dice similarity index (DSI) and volume error (VE). Results: Among all segmentation methods, the proposed one (DSI=0.81, VE=0.05) has the highest accuracy, followed by the active contours without edges (DSI=0.81, VE=0.25), while other methods including the Graph Cut and the Mumford-Shah (MS) method have lower accuracy. A visual inspection shows that the proposed method localizes the real tumor contour very well. Conclusion: The result showed that deconvolution and segmentation can contribute to each other. The proposed variational method solve the two problems simultaneously, and leads to a high performance for tumor segmentation in PET. This work was supported in part by National Natural Science Foundation of China (NNSFC), under Grant Nos. 60971112 and 61375018, and Fundamental Research Funds for the Central Universities, under Grant No. 2012QN086. Wei Lu was supported in part by the National Institutes of Health (NIH) Grant No. R01 CA172638.

SU‐E‐I‐96: A Study About the Influence of ROI Variation On Tumor Segmentation in PET

Purpose:To study the influence of different regions of interest (ROI) on tumor segmentation in PET.Methods:The experiments were conducted on a cylindrical phantom. Six spheres with different volumes (0.5ml, 1ml, 6ml, 12ml, 16ml and 20 ml) were placed inside a cylindrical container to mimic tumors of different sizes. The spheres were filled with 11C solution as sources and the cylindrical container was filled with 18F‐FDG solution as the background. The phantom was continuously scanned in a Biograph‐40 True Point/True View PET/CT scanner, and 42 images were reconstructed with source‐to‐background ratio (SBR) ranging from 16:1 to 1.8:1. We took a large and a small ROI for each sphere, both of which contain the whole sphere and does not contain any other spheres. Six other ROIs of different sizes were then taken between the large and the small ROI. For each ROI, all images were segmented by eitht thresholding methods and eight advanced methods, respectively. The segmentation results were evaluated by dice similarity index (DSI), classification error (CE) and volume error (VE). The robustness of different methods to ROI variation was quantified using the interrun variation and a generalized Cohen's kappa.Results:With the change of ROI, the segmentation results of all tested methods changed more or less. Compared with all advanced methods, thresholding methods were less affected by the ROI change. In addition, most of the thresholding methods got more accurate segmentation results for all sphere sizes.Conclusion:The results showed that the segmentation performance of all tested methods was affected by the change of ROI. Thresholding methods were more robust to this change and they can segment the PET image more accurately.This work was supported in part by National Natural Science Foundation of China (NNSFC), under Grant Nos. 60971112 and 61375018, and Fundamental Research Funds for the Central Universities, under Grant No. 2012QN086. Wei Lu was supported in part by the National Institutes of Health (NIH) Grant No. R01 CA172638.

Automated segmentation of visceral and subcutaneous (deep and superficial) adipose tissues in normal and overweight men

To develop an automatic segmentation algorithm to classify abdominal adipose tissues into visceral fat (VAT), deep (DSAT), and superficial (SSAT) subcutaneous fat compartments and evaluate its performance against manual segmentation. Data were acquired from 44 normal (BMI 18.0-22.9 kg/m(2) ) and 38 overweight (BMI 23.0-29.9 kg/m(2) ) subjects at 3T using a two-point Dixon sequence. A fully automatic segmentation algorithm was developed to segment the fat depots. The first part of the segmentation used graph cuts to separate the subcutaneous and visceral adipose tissues and the second step employed a modified level sets approach to classify deep and superficial subcutaneous tissues. The algorithmic results of segmentation were validated against the ground truth generated by manual segmentation. The proposed algorithm showed good performance with Dice similarity indices of VAT/DSAT/SSAT: 0.92/0.82/0.88 against the ground truth. The study of the fat distribution showed that there is a steady increase in the proportion of DSAT and a decrease in the proportion of SSAT with increasing obesity. The presented technique provides an accurate approach for the segmentation and quantification of abdominal fat depots.

Supervised methods for detection and segmentation of tissues in clinical lumbar MRI

Lower back pain (LBP) is widely prevalent all over the world and more than 80% of the people suffer from LBP at some point of their lives. Moreover, a shortage of radiologists is the most pressing cause for the need of CAD (computer-aided diagnosis) systems. Automatic localization and labeling of intervertebral discs from lumbar MRI is the first step towards computer-aided diagnosis of lower back ailments. Subsequently, for diagnosis and characterization (quantification and localization) of abnormalities like disc herniation and stenosis, a completely automatic segmentation of intervertebral discs and the dural sac is extremely important. Contribution of this paper towards clinical CAD systems is two-fold. First, we propose a method to automatically detect all visible intervertebral discs in clinical sagittal MRI using heuristics and machine learning techniques. We provide a novel end-to-end framework that outputs a tight bounding box for each disc, instead of simply marking the centroid of discs, as has been the trend in the recent past. Second, we propose a method to simultaneously segment all the tissues (vertebrae, intervertebral disc, dural sac and background) in a lumbar sagittal MRI, using an auto-context approach instead of any explicit shape features or models. Past work tackles the lumbar segmentation problem on a tissue/organ basis, and which tend to perform poorly in clinical scans due to high variability in appearance. We, on the other hand, train a series of robust classifiers (random forests) using image features and sparsely sampled context features, which implicitly represent the shape and configuration of the image. Both these methods have been tested on a huge clinical dataset comprising of 212 cases and show very promising results for both disc detection (98% disc localization accuracy and 2.08mm mean deviation) and sagittal MRI segmentation (dice similarity indices of 0.87 and 0.84 for the dural sac and the inter-vertebral disc, respectively).

Development and evaluation of an articulated registration algorithm for human skeleton registration

Accurate registration over multiple scans is necessary to assess treatment response of bone diseases (e.g. metastatic bone lesions). This study aimed to develop and evaluate an articulated registration algorithm for the whole-body skeleton registration in human patients. In articulated registration, whole-body skeletons are registered by auto-segmenting into individual bones using atlas-based segmentation, and then rigidly aligning them. Sixteen patients (weight = 80–117 kg, height = 168–191 cm) with advanced prostate cancer underwent the pre- and mid-treatment PET/CT scans over a course of cancer therapy. Skeletons were extracted from the CT images by thresholding (HU>150). Skeletons were registered using the articulated, rigid, and deformable registration algorithms to account for position and postural variability between scans. The inter-observers agreement in the atlas creation, the agreement between the manually and atlas-based segmented bones, and the registration performances of all three registration algorithms were all assessed using the Dice similarity index—DSIobserved, DSIatlas, and DSIregister. Hausdorff distance (dHausdorff) of the registered skeletons was also used for registration evaluation. Nearly negligible inter-observers variability was found in the bone atlases creation as the DSIobserver was 96 ± 2%. Atlas-based and manual segmented bones were in excellent agreement with DSIatlas of 90 ± 3%. Articulated (DSIregsiter = 75 ± 2%, dHausdorff = 0.37 ± 0.08 cm) and deformable registration algorithms (DSIregister = 77 ± 3%, dHausdorff = 0.34 ± 0.08 cm) considerably outperformed the rigid registration algorithm (DSIregsiter = 59 ± 9%, dHausdorff = 0.69 ± 0.20 cm) in the skeleton registration as the rigid registration algorithm failed to capture the skeleton flexibility in the joints. Despite superior skeleton registration performance, deformable registration algorithm failed to preserve the local rigidity of bones as over 60% of the skeletons were deformed. Articulated registration is superior to rigid and deformable registrations by capturing global flexibility while preserving local rigidity inherent in skeleton registration. Therefore, articulated registration can be employed to accurately register the whole-body human skeletons, and it enables the treatment response assessment of various bone diseases.

Quantitative Tumor Segmentation for Evaluation of Extent of Glioblastoma Resection to Facilitate Multisite Clinical Trials

Standard-of-care therapy for glioblastomas, the most common and aggressive primary adult brain neoplasm, is maximal safe resection, followed by radiation and chemotherapy. Because maximizing resection may be beneficial for these patients, improving tumor extent of resection (EOR) with methods such as intraoperative 5-aminolevulinic acid fluorescence-guided surgery (FGS) is currently under evaluation. However, it is difficult to reproducibly judge EOR in these studies due to the lack of reliable tumor segmentation methods, especially for postoperative magnetic resonance imaging (MRI) scans. Therefore, a reliable, easily distributable segmentation method is needed to permit valid comparison, especially across multiple sites. We report a segmentation method that combines versatile region-of-interest blob generation with automated clustering methods. We applied this to glioblastoma cases undergoing FGS and matched controls to illustrate the method's reliability and accuracy. Agreement and interrater variability between segmentations were assessed using the concordance correlation coefficient, and spatial accuracy was determined using the Dice similarity index and mean Euclidean distance. Fuzzy C-means clustering with three classes was the best performing method, generating volumes with high agreement with manual contouring and high interrater agreement preoperatively and postoperatively. The proposed segmentation method allows tumor volume measurements of contrast-enhanced T1-weighted images in the unbiased, reproducible fashion necessary for quantifying EOR in multicenter trials.

Reliability and repeatability of quantitative tractography methods for mapping structural white matter connectivity in preterm and term infants at term-equivalent age.

Premature infants exhibit widespread insults and delays in white matter maturation that can be sensitively detected early using diffusion tensor imaging. Diffusion tensor tractography facilitates in vivo visualization of white matter tracts and has the potential to be more sensitive than simpler two-dimensional DTI-based measures. However, the reliability and reproducibility of performing tractography for major white matter tracts in preterm infants is not known. The main objective of our study was to develop highly reliable and repeatable methods for ten white matter tracts in extremely low birth weight infants (birth weight ≤1000 g) at term-equivalent age. To demonstrate clinical utility, we also compared fiber microstructural and macrostructural parameters between preterm and healthy term controls. Twenty-nine ELBW infants and a control group of 15 healthy term newborns were studied. A team of researchers experienced in neuroanatomy/neuroimaging established the manual segmentation protocol based on a priori anatomical knowledge and an extensive training period to identify sources of variability. Intra- and inter-rater reliability and repeatability was tested using intra-class correlation coefficient, within-subject standard deviation (SD), repeatability, and Dice similarity index. Our results support our primary goal of developing highly reliable and reproducible comprehensive methods for manual segmentation of 10 white matter tracts in ELBW infants. The within-subject SD was within 1–2% and repeatability within 3–7% of the mean values for all 10 tracts. The intra-rater Dice index was excellent with a range of 0.97 to 0.99, and as expected, the inter-rater Dice index was lower (range: 0.80 to 0.91), but still within a very good reliability range. ELBW infants exhibited fewer fiber numbers and/or abnormal microstructure in a majority of the ten quantified tracts, consistent with injury/delayed development. This protocol could serve as a valuable tool for prompt evaluation of the impact of neuroprotective therapies and as a prognostic biomarker for neurodevelopmental impairments.

Evaluation of the Block Matching deformable registration algorithm in the field of head-and-neck adaptive radiotherapy

Background and purposeTo compare the accuracy of the Block Matching deformable registration (DIR) against rigid image registration (RIR) for head-and-neck multi-modal images CT to cone-beam CT (CBCT) registration. Material and methodsPlanning-CT and weekly CBCT of 10 patients were used for this study. Several volumes, including medullary canal (MC), thyroid cartilage (TC), hyoid bone (HB) and submandibular gland (SMG) were transposed from CT to CBCT images using either DIR or RIR. Transposed volumes were compared with the manual delineation of these volumes on every CBCT. The parameters of similarity used for analysis were: Dice Similarity Index (DSI), 95%-Hausdorff Distance (95%-HD) and difference of volumes (cc). ResultsWith DIR, the major mean difference of volumes was −1.4 cc for MC, revealing limited under-segmentation. DIR limited variability of DSI and 95%-HD. It significantly improved DSI for TC and HB and 95%-HD for all structures but SMG. With DIR, mean 95%-HD (mm) was 3.01 ± 0.80, 5.33 ± 2.51, 4.99 ± 1.69, 3.07 ± 1.31 for MC, TC, HB and SMG, respectively. With RIR, it was 3.92 ± 1.86, 6.94 ± 3.98, 6.44 ± 3.37 and 3.41 ± 2.25, respectively. ConclusionBlock Matching is a valid algorithm for deformable multi-modal CT to CBCT registration. Values of 95%-HD are useful for ongoing development of its application to the cumulative dose calculation.

Automatic delineation for replanning in nasopharynx radiotherapy: What is the agreement among experts to be considered as benchmark?

Background and purpose. Anatomic changes during head and neck radiotherapy require replanning. The primary aim of this study is the definition of the agreement among experts in the head and neck automatic delineation frame to use as benchmark. The secondary goal is to assess the reliability of automatic delineation for nasopharynx radiotherapy and time saving. Material and methods. A computed tomography (CT) scan was acquired in 10 nasopharynx patients along intensity-modulated radiotherapy (IMRT) treatment for replanning. Deformable registration with replanning autocontouring of the structures was performed using VelocityAI 2.3© software defining Structure Set A. The optimization of these contours was obtained through revision by a skilled operator, drawing Structure Set B. An ex novo Structure Set C was segmented on the replanning CT-scan by an expert delineation team. The mean Dice's Similarity Index (mDSI) was calculated between Structure Set A and B, A and C, and between B and C for each volume. All segmentation times for organs at risk (OARs) and clinical target volume (CTV) were recorded and compared. Results. We validated the replanning autocontoured Structure Sets for 10 patients. For volumetric analysis we observed mDSI values of 0.87 for the OARs, 0.70 for nodes, 0.90 for CTV in the Structure Set A-B comparison and respectively of 0.74, 0.63 and 0.78 for the Structure Set A-C one, and 0.78, 0.78 and 0.85 for Structure Set B-C, which represents the existing expert based benchmark. We calculated a mean saved time in Structure Set B of 30 minutes. Conclusions. Autocontouring procedures offer considerable segmentation time saving with acceptable reliability of the contours, even if an independent check procedure for their optimization is still required to increase their adherence to referential benchmark gold standard among experts, which stands at a 0.80 DSI value.

Automatic quantification of epicardial fat volume on non‐enhanced cardiac CT scans using a multi‐atlas segmentation approach

There is increasing evidence that epicardial fat (i.e., adipose tissue contained within the pericardium) plays an important role in the development of cardiovascular disease. Obtaining the epicardial fat volume from routinely performed non-enhanced cardiac CT scans is therefore of clinical interest. The purpose of this work is to investigate the feasibility of automatic pericardium segmentation and subsequent quantification of epicardial fat on non-enhanced cardiac CT scans. Imaging data of 98 randomly selected subjects belonging to a larger cohort of subjects who underwent a cardiac CT scan at our medical center were retrieved. The data were acquired on two different scanners. Automatic multi-atlas based method for segmenting the pericardium and calculating the epicardial fat volume has been developed. The performance of the method was assessed by (1) comparing the automatically segmented pericardium to a manually annotated reference standard, (2) comparing the automatically obtained epicardial fat volumes to those obtained manually, and (3) comparing the accuracy of the automatic results to the inter-observer variability. Automatic segmentation of the pericardium was achieved with a Dice similarity index of 89.1 ± 2.6% with respect to Observer 1 and 89.2 ± 1.9% with respect to Observer 2. The correlation between the automatic method and the manual observers with respect to the epicardial fat volume computed as the Pearson's correlation coefficient (R) was 0.91 (P < 0.001) for both observers. The inter-observer study resulted in a Dice similarity index of 89.0 ± 2.4% for segmenting the pericardium and a Pearson's correlation coefficient of 0.92 (P<0.001) for computation of the epicardial fat volume. The authors developed a fully automatic method that is capable of segmenting the pericardium and quantifying epicardial fat on non-enhanced cardiac CT scans. The authors demonstrated the feasibility of using this method to replace manual annotations by showing that the automatic method performs as good as manual annotation on a large dataset.

SU‐E‐J‐115: A Comparison of Methods for Functional Volume Delineation in PET: Simple Vs. Advanced Ones

Purpose: To examine the importance of taking into account partial volume effect (PVE) for functional volume delineation in PET. Methods: Five acquisitions with different source‐to‐background ratio (SBR) from a PET phantom were used as test data in this study. The phantom contains six spheres of different diameters. A total of eight methods were used to delineate the spheres from each acquisition. Five among the eight are simple thresholding methods and the other three are advanced methods which were well‐known and widely used in the image processing community. The five thresholding methods are the dynamic thresholding, iterative thresholding, thresholding with 42% and 50% of the maximum uptake and the Otsu method. The three advanced methods are active contours without edges (CV), geodesic active contours (GAC) and max‐flow/min‐cut graph cut. The first two thresholding methods take into account the partial volume effect for the delineation while the other six do not. Robustness of different methods with respect to SBR and object size was evaluated using dice similarity index (DSI), classification error (CE) and volume error (VE). Results: The first two thresholding methods outperformed all the other six methods at all SBR levels and for all object sizes according to DSI, CE and VE. The first two thresholding methods were also less sensitive to the change of object size. Conclusion: It has been widely accepted that advanced methods should outperform simple thresholding ones for functional volume delineation in PET. The results indicated, however, that simple thresholding methods may not be inferior to advance methods once these thresholding methods take into account the PVE. It is thus important to consider the PVE for functional volume image delineation in PET.

Fingerprinting and genetic diversity of Olea europaea L. ssp. europaea accessions from the cultivar Galega using RAPD markers

Olea europaea ssp. europaea (Oleaceae) grows in Mediterranean countries. The Galega represents one of the most common Portuguese olive tree cultivars, and is used in five out of the six PDO (Protected Designation of Origin) regions. The fruits are used for olive oil and for fruit canning, besides which the cultivar is drought tolerant. The cultivar certification is done through phenotypic characteristics, but environmental influence might blur informativeness, and molecular markers could help in clarifying cultivar accessions certification.A total of 75 putative Galega accessions considered as belonging to the Galega cultivar, three previously phenotypically identified as GGN (Galega Grada Normal), GGS (Galega Grada de Serpa) and GGE (Galega Grada de Évora), and one as a non-Galega blind control were analyzed. Twenty RAPD primers amplified 144 bands and 32% were polymorphic. A low expected heterozygosity was estimated (10%), as expected for clonal propagated plants.The Principal Component Analysis (PCA) clearly identified two groups. Group A was comprised of all the putative Galega accessions including GGN, but not individual #502. Group B included NG, GGE, GGS and #502. The analysis of molecular variance confirmed a very high differentiation between the two groups (88%), but also some within groups’ diversity (12%). Group A individuals cluster closely to GGN. Assuming that the GGN individual represents a Galega cultivar tree, we might assume that all the A group trees are Galega cultivar accessions. Nevertheless, some within group variability exists, may be due to the accumulation of somatic mutations and/or a putative origin from different, but related genotypes. Finally, the GGS, the GGE, and the #502 plot together with the individual identified as non Galega cultivar tree. Group B trees certainly had a very different genetic origin compared to the group A genotypes, which confirms some differences found in the morphologic characteristics compared to the ordinary Galega cultivar, reflected in the name differences.The pairwise matrix computed with the Dice similarity index was also used to obtain a dendrogram. Results were similar, but slightly different from the PCA. Three clusters were obtained. This indicates that the B group could be split into two sub-groups. A future study using a broader sampling and higher discriminant molecular markers, such as microsatellites, would help to reveal the phylogeny among the 3 cultivars (GGN, GGS and GGE) and to clarify if the intra-variability of those cultivars is geographically related.

Abstract 734: Water diffusion decreased in low collagen containing hypoxic regions of breast cancer xenograft.

Abstract Tumors display chaotic vasculature that leads to hypoxia. Hypoxia is associated with an aggressive phenotype and increased resistance to radiation and chemotherapy. Hypoxic tumor regions also contain very few collagen 1 fibers, which is a major component of the tumor extracellular matrix (ECM) and plays an important role in molecular movement through the ECM. Here we examined the effects of hypoxia on water diffusion in a breast cancer xenograft model engineered to fluoresce under hypoxia. We observed, for the first time, that in hypoxic regions water diffusion and diffusion anisotropy was lower than in normoxic regions that may be explained by the few collagen fibers in hypoxic regions. Severe combined immunodeficient female mice were inoculated in the mammary fat pad with 2x106 MDA-MB-231 cells stably expressing red fluorescent protein (RFP) under the control of a hypoxia response element. Once tumor volume was approximately 300-400 mm3 the tumor was excised, fixed and imaged using high-resolution diffusion tensor imaging (DTI). The apparent diffusion coefficient (ADC) and fractional anisotropy (FA) maps were calculated. Following DTI, the tumor was sliced at 1 mm thick sections for optical imaging. Using a 1x lens to cover the entire tumor section, bright field and fluorescence optical images were acquired to detect the RFP-containing hypoxic regions. Multimodality registration was performed by feature extraction to co-register the optical images to the DTI images. Statistical dice similarity indices were calculated as a measure of the registration error. Hypoxic regions were identified from the optical fluorescence field images and the ADC and FA values were calculated in the corresponding hypoxic and normoxic regions. ADC and FA value distributions were identified in the DTI data and red fluorescent hypoxic regions were identified from the optical images. Co-registration accuracy confirmed by dice similarity index of 0.88 indicates a good overlap of optical and DTI images. Overall, hypoxic regions had lower water diffusion (ADC) and diffusion anisotropy (FA) values than normoxic regions. We have consistently observed that collagen fibers are significantly decreased in hypoxic regions, which could explain the lower ADC and FA values observed in hypoxic regions. In separate studies we have also observed high ADC in high collagen fiber containing regions in human breast cancer tissue. Low ADC and FA values observed in low collagen containing hypoxic regions indicate a functional role of these fibers in molecular transport. Decreased diffusion of molecules due to fewer collagen fibers in hypoxic regions may also contribute to poor drug delivery and tumor recurrence in hypoxic regions. Increased cellularity is thought to be one reason why lower ADC and FA values are observed in tumors. An even lower ADC within this backdrop may be due to hypoxic tumor regions. This work was supported by NIH P50CA103175 P30CA006973 Citation Format: Samata M. Kakkad, Jiangyang Zhang, Alireza Akhbardeh, Meiyappan Solaiyappan, Venu Raman, Dieter Leibfritz, Kristine Glunde, Zaver M. Bhujwalla. Water diffusion decreased in low collagen containing hypoxic regions of breast cancer xenograft. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 734. doi:10.1158/1538-7445.AM2013-734

Early Identification of Potentially Salvageable Tissue with MRI-Based Predictive Algorithms after Experimental Ischemic Stroke

Individualized stroke treatment decisions can be improved by accurate identification of the extent of salvageable tissue. Magnetic resonance imaging (MRI)-based approaches, including measurement of a 'perfusion-diffusion mismatch' and calculation of infarction probability, allow assessment of tissue-at-risk; however, the ability to explicitly depict potentially salvageable tissue remains uncertain. In this study, five predictive algorithms (generalized linear model (GLM), generalized additive model, support vector machine, adaptive boosting, and random forest) were tested in their potency to depict acute cerebral ischemic tissue that can recover after reperfusion. Acute T2-, diffusion-, and perfusion-weighted MRI, and follow-up T2 maps were collected from rats subjected to right-sided middle cerebral artery occlusion without subsequent reperfusion, for training of algorithms (Group I), and with spontaneous (Group II) or thrombolysis-induced reperfusion (Group III), to determine infarction probability-based viability thresholds and prediction accuracies. The infarction probability difference between irreversible-i.e., infarcted after reperfusion-and salvageable tissue injury-i.e., noninfarcted after reperfusion-was largest for GLM (20±7%) with highest accuracy of risk-based identification of acutely ischemic tissue that could recover on subsequent reperfusion (Dice's similarity index=0.79±0.14). Our study shows that assessment of the heterogeneity of infarction probability with MRI-based algorithms enables estimation of the extent of potentially salvageable tissue after acute ischemic stroke.