Atlas Segmentation Research Articles

Hippocampal subfields and thalamic nuclei associations with clinical outcomes in multiple sclerosis: An ultrahigh field MRI study

BackgroundPrevious studies have shown that thalamic and hippocampal neurodegeneration is associated with clinical decline in Multiple Sclerosis (MS). However, contributions of the specific thalamic nuclei and hippocampal subfields require further examination. ObjectiveUsing 7 Tesla (7T) magnetic resonance imaging (MRI), we investigated the cross-sectional associations between functionally grouped thalamic nuclei and hippocampal subfields volumes and T1 relaxation times (T1-RT) and subsequent clinical outcomes in MS. MethodsHigh-resolution T1-weighted and T2-weighted images were acquired at 7T (n=31), preprocessed, and segmented using the Thalamus Optimized Multi Atlas Segmentation (THOMAS, for thalamic nuclei) and the Automatic Segmentation of Hippocampal Subfields (ASHS, for hippocampal subfields) packages. We calculated Pearson correlations between hippocampal subfields and thalamic nuclei volumes and T1-RT and subsequent multi-modal rater-determined and patient-reported clinical outcomes (∼2.5 years after imaging acquisition), correcting for confounders and multiple tests. ResultsSmaller volume bilaterally in the anterior thalamus region correlated with worse performance in gait function, as measured by the Patient Determined Disease Steps (PDDS). Additionally, larger volume in most functional groups of thalamic nuclei correlated with better visual information processing and cognitive function, as measured by the Symbol Digit Modalities Test (SDMT). In bilateral medial and left posterior thalamic regions, there was an inverse association between volumes and T1-RT, potentially indicating higher tissue degeneration in these regions. We also observed marginal associations between the right hippocampal subfields (both volumes and T1-RT) and subsequent clinical outcomes, though they did not survive correction for multiple testing. ConclusionUltrahigh field MRI identified markers of structural damage in the thalamic nuclei associated with subsequently worse clinical outcomes in individuals with MS. Longitudinal studies will enable better understanding of the role of microstructural integrity in these brain regions in influencing MS outcomes.

GiRAff: an automated atlas segmentation tool adapted to single histological slices.

Conventional histology of the brain remains the gold standard in the analysis of animal models. In most biological studies, standard protocols usually involve producing a limited number of histological slices to be analyzed. These slices are often selected into a specific anatomical region of interest or around a specific pathological lesion. Due to the lack of automated solutions to analyze such single slices, neurobiologists perform the segmentation of anatomical regions manually most of the time. Because the task is long, tedious, and operator-dependent, we propose an automated atlas segmentation method called giRAff, which combines rigid and affine registrations and is suitable for conventional histological protocols involving any number of single slices from a given mouse brain. In particular, the method has been tested on several routine experimental protocols involving different anatomical regions of different sizes and for several brains. For a given set of single slices, the method can automatically identify the corresponding slices in the mouse Allen atlas template with good accuracy and segmentations comparable to those of an expert. This versatile and generic method allows the segmentation of any single slice without additional anatomical context in about 1 min. Basically, our proposed giRAff method is an easy-to-use, rapid, and automated atlas segmentation tool compliant with a wide variety of standard histological protocols.

An open source auto-segmentation algorithm for delineating heart and substructures – Development and validation within a multicenter lung cancer cohort

Background and purposeIrradiation of the heart in thoracic cancers raises toxicity concerns. For accurate dose estimation, automated heart and substructure segmentation is potentially useful. In this study, a hybrid automatic segmentation is developed. The accuracy of delineation and dose predictions were evaluated, testing the method's potential within heart toxicity studies. Materials and MethodsThe hybrid segmentation method delineated the heart, four chambers, three large vessels, and the coronary arteries. The method consisted of a nnU-net heart segmentation and partly atlas- and model-based segmentation of the substructures. The nnU-net training and atlas segmentation was based on lung cancer patients and was validated against a national consensus dataset of 12 patients with breast cancer. The accuracy of dose predictions between manual and auto-segmented heart and substructures was evaluated by transferring the dose distribution of 240 previously treated lung cancer patients to the consensus data set. ResultsThe hybrid auto-segmentation method performed well with a heart dice similarity coefficient (DSC) of 0.95, with no statistically significant difference between the automatic and manual delineations. The DSC for the chambers varied from 0.78-0.86 for the automatic segmentation and was comparable with the inter-observer variability. Most importantly, the automatic segmentation was as precise as the clinical experts in predicting the dose distribution to the heart and all substructures. ConclusionThe hybrid segmentation method performed well in delineating the heart and substructures. The prediction of dose by the automatic segmentation was aligned with the manual delineations, enabling measurement of heart and substructure dose in large cohorts. The delineation algorithm will be available for download.

Cortical and subcortical morphometric changes and their relation to cognitive impairment in isolated REM sleep behavior disorder.

To date, very few studies have focused on structural changes and their association with cognitive performance in isolated REM sleep behaviour disorder (iRBD). Moreover, the results of these studies are inconclusive. This study aims to evaluate differences in the associations between brain morphology and cognitive tests in iRBD and healthy controls. Sixty-three patients with iRBD and thirty-six controls underwent MRI with a 3T scanner. The cognitive performance was assessed by a comprehensive neuropsychological battery. Based on performance, the iRBD group was divided into two subgroups with (iRBD-MCI) and without mild cognitive impairment (iRBD-NC). The high-resolution T1-weighted images were analysed using an automated atlas segmentation tool, voxel-based (VBM) and deformation-based (DBM) morphometry to identify between-group differences and correlations with cognitive performance. VBM, DBM and the comparison of ROI volumes yielded no significant differences between iRBD and controls. In the iRBD group, significant correlations in VBM were found between several cortical and subcortical structures primarily located in the temporal, parietal, occipital lobe, cerebellum, and basal ganglia and three cognitive tests assessing psychomotor speed and one memory test. Between-group analysis of cognition revealed a significant difference between iRBD-MCI and iRBD-NC in tests including a processing speed component. iRBD shows deficits in several cognitive tests that correlate with morphological changes, the most prominent of which is in psychomotor speed and visual attention as measured by the TMT-A and associated with the volume of striatum, insula, cerebellum, temporal lobe, pallidum and amygdala.

Thalamic nuclei changes in early and late onset Alzheimer's disease.

Alzheimer's disease (AD) is the most common cause of dementia worldwide. Increasing evidence points to the thalamus as an important hub in the clinical symptomatology of the disease, with the 'limbic thalamus' been described as especially vulnerable. In this work, we examined thalamic atrophy in early-onset AD (EOAD) and late-onset AD (LOAD) compared to young and old healthy controls (YHC and OHC, respectively) using a recently developed cutting-edge thalamic nuclei segmentation method. A deep learning variant of Thalamus Optimized Multi Atlas Segmentation (THOMAS) was used to parcellate 11 thalamic nuclei per hemisphere from T1-weighted MRI in 88 biomarker-confirmed AD patients (49 EOAD and 39 LOAD) and 58 healthy controls (41 YHC and 17 OHC) with normal AD biomarkers. Nuclei volumes were compared among groups using MANCOVA. Further, Pearson's correlation coefficient was computed between thalamic nuclear volume and cortical-subcortical regions, CSF tau levels, and neuropsychological scores. The results showed widespread thalamic nuclei atrophy in EOAD and LOAD compared to their respective healthy control groups, with EOAD showing additional atrophy in the centromedian and ventral lateral posterior nuclei compared to YHC. In EOAD, increased thalamic nuclei atrophy was associated with posterior parietal atrophy and worse visuospatial abilities, while LOAD thalamic nuclei atrophy was preferentially associated with medial temporal atrophy and worse episodic memory and executive function. Our findings suggest that thalamic nuclei may be differentially affected in AD according to the age at symptoms onset, associated with specific cortical-subcortical regions, CSF total tau and cognition.

Atlas-Based Segmentation in Extraction of Knee Joint Bone Structures from CT and MR.

The main goal of the approach proposed in this study, which is dedicated to the extraction of bone structures of the knee joint (femoral head, tibia, and patella), was to show a fully automated method of extracting these structures based on atlas segmentation. In order to realize the above-mentioned goal, an algorithm employed automated image-matching as the first step, followed by the normalization of clinical images and the determination of the 11-element dataset to which all scans in the series were allocated. This allowed for a delineation of the average feature vector for the teaching group in the next step, which automated and streamlined known fuzzy segmentation methods (fuzzy c-means (FCM), fuzzy connectedness (FC)). These averaged features were then transmitted to the FCM and FC methods, which were implemented for the testing group and correspondingly for each scan. In this approach, two features are important: the centroids (which become starting points for the fuzzy methods) and the surface area of the extracted bone structure (protects against over-segmentation). This proposed approach was implemented in MATLAB and tested in 61 clinical CT studies of the lower limb on the transverse plane and in 107 T1-weighted MRI studies of the knee joint on the sagittal plane. The atlas-based segmentation combined with the fuzzy methods achieved a Dice index of 85.52-89.48% for the bone structures of the knee joint.

Clinical evaluation of deep learning and atlas-based auto-segmentation for critical organs at risk in radiation therapy.

Contouring organs at risk (OARs) is a time-intensive task that is a critical part of radiation therapy. Atlas-based automatic segmentation has shown some success at reducing this time burden on practitioners; however, this method often requires significant manual editing to reach a clinically accurate standard. Deep learning (DL) auto-segmentation has recently emerged as a promising solution. This study compares the accuracy of DL and atlas-based auto-segmentation in relation to clinical 'gold standard' reference contours. Ninety CT datasets (30 head and neck, 30 thoracic, 30 pelvic) were automatically contoured using both atlas and DL segmentation techniques. Sixteen critical OARs were then quantitatively measured for accuracy using the Dice similarity coefficient (DSC) and Hausdorff distance (HD). Qualitative analysis was performed to visually classify the accuracy of each structure into one of four explicitly defined categories. Additionally, the time to edit atlas and DL contours to a clinically acceptable level was recorded for a subset of 9 OARs. Of the 16 OARs analysed, DL delivered statistically significant improvements over atlas segmentation in 13 OARs measured with DSC, 12 OARs measured with HD, and 12 OARs measured qualitatively. The mean editing time for the subset of DL contours was 50%, 23% and 61% faster (all P < 0.05) than that of atlas segmentation for the head and neck, thorax, and pelvis respectively. Deep learning segmentation comprehensively outperformed atlas-based contouring for the majority of evaluated OARs. Improvements were observed in geometric accuracy and visual acceptability, while editing time was reduced leading to increased workflow efficiency.

Atlas-based lung segmentation combined with automatic densitometry characterization in COVID-19 patients: Training, validation and first application in a longitudinal study

PurposeTo develop and validate an automated segmentation tool for COVID-19 lung CTs. To combine it with densitometry information in identifying Aerated, Intermediate and Consolidated Volumes in admission (CT1) and follow up CT (CT3). Materials and MethodsAn Atlas was trained on manually segmented CT1 of 250 patients and validated on 10 CT1 of the training group, 10 new CT1 and 10 CT3, by comparing DICE index between automatic (AUTO), automatic-corrected (AUTOMAN) and manual (MAN) contours. A previously developed automatic method was applied on HU lung density histograms to quantify Aerated, Intermediate and Consolidated Volumes. Volumes of subregions in validation CT1 and CT3 were quantified for each method. ResultsIn validation CT1/CT3, manual correction of automatic contours was not necessary in 40% of cases. Mean DICE values for both lungs were 0.94 for AUTOVsMAN and 0.96 for AUTOMANVsMAN. Differences between Aerated and Intermediate Volumes quantified with AUTOVsMAN contours were always < 6%. Consolidated Volumes showed larger differences (mean: −95 ± 72 cc). If considering AUTOMANVsMAN volumes, differences got further smaller for Aerated and Intermediate, and were drastically reduced for consolidated Volumes (mean: −36 ± 25 cc). The average time for manual correction of automatic lungs contours on CT1 was 5 ± 2 min. ConclusionsAn Atlas for automatic segmentation of lungs in COVID-19 patients was developed and validated. Combined with a previously developed method for lung densitometry characterization, it provides a fast, operator-independent way to extract relevant quantitative parameters with minimal manual intervention.

P.124 Subnuclear contact localization within the subthalamic nucleus in deep brain stimulation for Parkinson Disease

Background: Therapeutic response from subthalamic nucleus (STN) deep brain stimulation (DBS) for Parkinson disease (PD) has been associated with proximity to an ideal target, commonly in the dorsal sensorimotor STN. Automated registration and atlas-based segmentation has allowed for contact localization within STN subnuclei. We sought to apply these methods to characterize the spatial distribution of our active contact placements. Methods: We conducted a retrospective analysis of 55 patients who underwent bilateral STN DBS for PD. Post-operative CT/MRI scans were non-linearly registered into a standard space, and DBS-electrodes were localized using Lead-DBS. 3-dimensional meshes from a segmented atlas (Ewert 2017) were utilized. Analysis was performed in MATLAB R2019b. Results: Mean active contacts were within sensorimotor STN bilaterally, located posteroinferiorly compared to reported ideal targets. Centroids fell within (left/right): sensorimotor (46%/40%), associative (22%/22%), limbic (0%/2%) and outside STN (32%/36%). Principal components analysis demonstrated most spatial variance is explained by the first component (left 65.8%, right 61.9%). Conclusions: We obtained contact locations in relation to STN subnuclei, allowing for an anatomically guided approach to our analysis. 66% of the active contacts were located within the STN, and most of the spatial variation occurred along a single dimension. Future directions include utilizing subnuclei localizations to investigate clinical outcomes.

FASTMAP: Open-Source Flexible Atlas Segmentation Tool for Multi-Area Processing of Biological Images

To better understand complex systems, such as the brain, studying the interactions between multiple brain regions is imperative. Such experiments often require delineation of multiple brain regions on microscopic images based on preexisting brain atlases. Experiments examining the relationships of multiple regions across the brain have traditionally relied on manual plotting of regions. This process is very intensive and becomes untenable with a large number of regions of interest (ROIs). To reduce the amount of time required to process multi-region datasets, several tools for atlas registration have been developed; however, these tools are often inflexible to tissue type, only supportive of a limited number of atlases and orientation, require considerable computational expertise, or are only compatible with certain types of microscopy. To address the need for a simple yet extensible atlas registration tool, we have developed FASTMAP, a Flexible Atlas Segmentation Tool for Multi-Area Processing. We demonstrate its ability to register images efficiently and flexibly to custom mouse brain atlas plates, to detect differences in the regional numbers of labels of interest, and to conduct densitometry analyses. This open-source and user-friendly tool will facilitate the atlas registration of diverse tissue types, unconventional atlas organizations, and a variety of tissue preparations.

Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging

Alzheimer’s disease is a neurodegenerative condition that accelerates cognitive decline relative to normal aging. It is of critical scientific importance to gain a better understanding of early disease mechanisms in the brain to facilitate effective, targeted therapies. The volume of the hippocampus is often used in diagnosis and monitoring of the disease. Measuring this volume via neuroimaging is difficult since each hippocampus must either be manually identified or automatically delineated, a task referred to as segmentation. Automatic hippocampal segmentation often involves mapping a previously manually segmented image to a new brain image and propagating the labels to obtain an estimate of where each hippocampus is located in the new image. A more recent approach to this problem is to propagate labels from multiple manually segmented atlases and combine the results using a process known as label fusion. To date, most label fusion algorithms employ voting procedures with voting weights assigned directly or estimated via optimization. We propose using a fully Bayesian spatial regression model for label fusion that facilitates direct incorporation of covariate information while making accessible the entire posterior distribution. Our results suggest that incorporating tissue classification (e.g., gray matter) into the label fusion procedure can greatly improve segmentation when relatively homogeneous, healthy brains are used as atlases for diseased brains. The fully Bayesian approach also produces meaningful uncertainty measures about hippocampal volumes, information which can be leveraged to detect significant, scientifically meaningful differences between healthy and diseased populations, improving the potential for early detection and tracking of the disease. Supplementary materials for this article are available online.

Optimal Atlas Segmentation on CT Images for Diagnosis of Pediatric Mycoplasma Pneumonia

This work aimed to explore the clinical application value of CT imaging technology based on the optimal Atlas segmentation algorithm (OASA) in the diagnosis of pediatric mycoplasma pneumonia (MP). Eighty-eight children with MP were selected and divided into group A (CT image based on the OASA) and group B (chest X-ray) according to the diagnosis methods. The detection rate, image feature performance, and image quality satisfaction of the two groups of children were compared. The results showed that the detection rate of group A was 97.73% and that of group B was 95.46%, and there was no considerable difference between the two ( P &gt; 0.05). The pleural effusion detection rate of children in group A was evidently superior to that of X-ray group, while the increased bronchovascular shadows’ detection rate was greatly inferior to that of X-ray group ( P &lt; 0.05). Comparison results of nodules’ shadows, patchy shadows, acinar parenchyma shadows, and interstitial infiltration between two groups showed that there was no notable difference ( P &gt; 0.05). CT image quality satisfaction (98.50%) was higher versus X-ray (79.46%) ( P &lt; 0.05). To sum up, CT images based on the OASA can be adopted in the clinical diagnosis of pediatric MP, and CT images were better than chest X-rays.

Retrospective study of late radiation-induced damages after focal radiotherapy for childhood brain tumors.

PurposeTo study a robust and reproducible procedure to investigate a relation between focal brain radiotherapy (RT) low doses, neurocognitive impairment and late White Matter and Gray Matter alterations, as shown by Diffusion Tensor Imaging (DTI), in children.Methods and materialsForty-five patients (23 males and 22 females, median age at RT 6.2 years, median age at evaluations 11.1 years) who had received focal RT for brain tumors were recruited for DTI exams and neurocognitive tests. Patients’ brains were parceled in 116 regions of interest (ROIs) using an available segmented atlas. After the development of an ad hoc, home-made, multimodal and highly deformable registration framework, we collected mean RT doses and DTI metrics values for each ROI. The pattern of association between cognitive scores or domains and dose or DTI values was assessed in each ROI through both considering and excluding ROIs with mean doses higher than 75% of the prescription. Subsequently, a preliminary threshold value of dose discriminating patients with and without neurocognitive impairment was selected for the most relevant associations.ResultsThe workflow allowed us to identify 10 ROIs where RT dose and DTI metrics were significantly associated with cognitive tests results (p<0.05). In 5/10 ROIs, RT dose and cognitive tests were associated with p<0.01 and preliminary RT threshold dose values, implying a possible cognitive or neuropsychological damage, were calculated. The analysis of domains showed that the most involved one was the “school-related activities”.ConclusionThis analysis, despite being conducted on a retrospective cohort of children, shows that the identification of critical brain structures and respective radiation dose thresholds is achievable by combining, with appropriate methodological tools, the large amount of data arising from different sources. This supported the design of a prospective study to gain stronger evidence.

Abstract PS15-16: Evaluation of workflow box mirada software for automatic delineation of left anterior descending coronary artery (LADCA) in order reduce cardiac toxicity for radiotherapy breast cancer treatments

Abstract Purpose/Objective: In order to preserve the patient's cardiac integrity, a good practice that would be simple and quick to implement would be to optimize dose on the left anterior descending coronary artery (LADCA) during dosimetric planning treatment. However, manual delineation of LADCA remains complicated to perform in clinical routine on non-injected CT-scans. To conclude on the feasibility of such a practice, we investigated the automatic delineation robustness of the Mirada software (Mirada Medical, UK) to generate these volumes. Materials/methods: Forty randomly selected CT-scans of breast cancer patients were used. All were delineated by a radiation therapist following international recommendations (Duane et al., 2017). A planning organ at risk volume (PRV) with a 10 mm margin centered on the LADCA was also performed. Among the forty CT-scans, twenty were used to develop the automatic segmentation atlas using “Workflow Box” auto-segmentation algorithm. The remaining twenty other CT-scans were compiled on the software in order to obtain auto-segmented structures. The relevance of the contours was carried out with the Artiview software (Aquilab, Loos-lès-Lille, France) in order to determine both Dice-similarity coefficients (DSC) and Jaccard indexes. Finally, dosimetric planning treatment in volumetric modulated arc therapy conditions was performed using Eclipse treatment planning system (Varian). Results: Because of variability, both in terms of shape and spatial localization, the atlas-based auto-segmentation software did not succeed in LADCA delineation. Then, LADCA contours were not available for DSC and Jaccard indexes assessment. On the other hand, the use of margins on the LADCA made it possible to create such an Atlas. The average DSC is 0.36 ± 0.15 and the Jaccard index reaches 0.23 ± 0.11. The results associated with dosimetry planning treatment are presented in Table 1. The use of a PRV instead of the LADCA contour tends to overestimate the maximum doses by around 30%. However, the mean and median doses are of the same order of magnitude. Conclusion: Small volume structures do not allow the creation of a robust contouring atlas. However, to overcome this limitation, the use of margins to define a PRV has been quite effective. However, it overestimates the maximum doses by about 30%, so the constraints used to limit doses to the coronary arteries during treatment planning will have to be adjusted in this way. Table 1 Dosimetric results from VMAT optimization for breast radiotherapy treatments.Left breastRight breastPRVLADCAPRVLADCAAverage dose (Gy)12.71 ± 6.5413.24 ± 7.485.86 ± 1.345.61 ± 1.39Median Dose (Gy)11.75 ± 6.5713.32 ± 8.335.75 ± 1.325.59 ± 1.27Maximum dose (Gy)33.29 ± 12.8525.28 ± 11.1810.25 ± 1.967.99 ± 2.09 Citation Format: Nicolas Tkatchenko, Pierre Loap, Eliot Nicolas, Youlia Kirova. Evaluation of workflow box mirada software for automatic delineation of left anterior descending coronary artery (LADCA) in order reduce cardiac toxicity for radiotherapy breast cancer treatments [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS15-16.

Application of CT images based on the optimal atlas segmentation algorithm in the clinical diagnosis of Mycoplasma Pneumoniae Pneumonia in Children.

Objective:Use of optimal Atlas segmentation algorithm to study the imaging signs of mycoplasma pneumonia with multi-slice spiral CT (HRCT), and to explore the value of HRCT in the diagnosis and efficacy in evaluation of mycoplasma pneumonia in children.Methods:The study retrospectively analyzed 72 patients diagnosed with mycoplasma pneumonia in our hospital from January 2017 to January 2019. The imaging data and clinical data of 72 patients were collected. The optimal Atlas segmentation algorithm was used to analyze the characteristics of CT examination, and the value of CT in the diagnosis of mycoplasma pneumonia and the evaluation of curative effect was summarized.Results:Among all patients, 37 cases were unilateral lesions, 35 cases were bilateral lesions, 19 cases were in the left upper lobe, 24 cases were in the left lower lobe, 21 cases were in the right upper lobe, 13 cases were in the right middle lobe, 25 The lesion was located in the right lower lobe. The main CT findings of the lesions before treatment were large patchy, spot-shaped shadows, and strip-shaped or ground-glass shadows. After treatment, the main CT findings of the lesions were reduced lesion density and reduced lesion range.Conclusion:CT can clearly show the pulmonary lesions of mycoplasma pneumonia, and its unique imaging signs can improve the clinical diagnosis accuracy. In addition, CT scans can evaluate the treatment effect according to the changes in the characteristics of the lesion, which has important value for the evaluation of the effect for clinical diagnosis and efficacy evaluation of mycoplasma pneumonia.

Validation of automated whole-body analysis of metabolic and morphological parameters from an integrated FDG-PET/MRI acquisition

Automated quantification of tissue morphology and tracer uptake in PET/MR images could streamline the analysis compared to traditional manual methods. To validate a single atlas image segmentation approach for automated assessment of tissue volume, fat content (FF) and glucose uptake (GU) from whole-body [18F]FDG-PET/MR images. Twelve subjects underwent whole-body [18F]FDG-PET/MRI during hyperinsulinemic-euglycemic clamp. Automated analysis of tissue volumes, FF and GU were achieved using image registration to a single atlas image with reference segmentations of 18 volume of interests (VOIs). Manual segmentations by an experienced radiologist were used as reference. Quantification accuracy was assessed with Dice scores, group comparisons and correlations. VOI Dice scores ranged from 0.93 to 0.32. Muscles, brain, VAT and liver showed the highest scores. Pancreas, large and small intestines demonstrated lower segmentation accuracy and poor correlations. Estimated tissue volumes differed significantly in 8 cases. Tissue FFs were often slightly but significantly overestimated. Satisfactory agreements were observed in most tissue GUs. Automated tissue identification and characterization using a single atlas segmentation performs well compared to manual segmentation in most tissues and will be valuable in future studies. In certain tissues, alternative quantification methods or improvements to the current approach is needed.

Abstract WP208: Lower Fractional Anisotropy in White Matter Tracts is Associated With Decreased Manipulative Manual Dexterity Higher Circulating Ceramide Levels in Healthy High-Risk Middle-Aged Individuals

Introduction: We previously reported impaired manipulative manual dexterity measured by grooved peg-board test (GPBT) in healthypeople with increased total brain ischemic white matter hyperintensities. We hypothesized that preclinical white matter tract damage (WMTD), measured as lower regional fractional anisotropy (rFA), is associated with functional decrements measured with GPBT, and with a biomarker of white matter disease, serum ceramides. Methods: Participants were recruited from the GeneSTAR study in families with a history of coronary artery disease. Diffusion tensor imaging was acquired on a Phillips 3T MRI and rFA was calculated using the 181 Eve segmented atlas. 48 susceptible regions for WMTD were analyzed.Liquid chromatography was used to measure ceramides (C). Spearman’s rank correlation (SRC) was used to test statistical associations and Bonferroni method was used to correct for multiple comparisons. Multivariate analysis (GEE) adjusting for age, sex, race and education was performed for associations that were significant in the univariate analyses. Result: Subjects (n=112) were 49±11 years; 49% male and 39% African American. rFA was correlated with decrements on GPBT in the left (L) anterior corona radiata, L genu of the corpus callosum (LGCC), L external capsule, and L &amp; right fornix (RF) (P=0.0009, 0.0008, 0.0002, 0.0003 and 8.2E-07, respectively). Additionally, rFA was negatively correlated with C18:0 in the RF and LGCC (P=1.27E-05 and 2.58E-05, respectively) and C20:0 in RF (P=6.09E-06). C18:0 was correlated with decrements on GPBT (P=0.0025). GEE showed associations between decremental GPBT and rFA in the RF (B = -0.52, 95% CI -1 - 0.03, p= 0.037), between rFA in the RF and Ceramide (C18:0, B= -0.03, 95% CI -0.046--0.0073, p=0.007, C20:0, B=-0.0002, 95%CI -0.0003-0, P= 0.018), as well as rFA in LGCC and Ceramide (C18:0, B=-0.0097, 95% CI -0.019--0.006, P=0.037). GBPT and ceramides were not associated when adjusted for rFA (B = 0.0002 95% CI -0.052 - 0.052 P= 0.99). Conclusion: These analyses support our hypothesis that preclinical WMTD represented by reduced rFA, may undermine neural networks affecting manipulative manual dexterity, with concomitant elevation in serum ceramides in healthy high risk people.

Automatic segmentation of organs at risk for nasopharyngeal carcinoma with Smart Segmentation and MIM Atlas

Objective To compare the accuracy of two automatic segmentation softwares (Smart Segmentation and MIM Atlas) in organs at risk (OARs) contouring for nasopharyngeal carcinoma (NPC). Methods Totally 55 NPC patients were retrospectively reviewed with manually contoured OARs on CT images, in which 30 cases were randomly selected to create a data base in the Smart Segmentation and MIM Atlas. The remaining 25 cases were automatically contoured with Smart Segmentation and MIM as test cases. The automatic contouring accuracies of two softwares were evaluated with Dice coefficient(DSC), Hausdorff distance(HD), and absolute volume difference(△V) using manual contours as a golden standard. Results The overall DSC, HD and △V of all organs contoured by MIM Atlas and Smart Segmentation were (0.79±0.13) vs. (0.62±0.24) (t=14.06, P<0.05), (5.50±3.84)mm vs.(8.38±4.88)mm (t=-11.40, P<0.05), and (1.52±2.46) cm3vs. (2.38±3.57) cm3 (t=-4.70, P<0.05), respectively. The average DSC of 11 organs (brain stem, optic chiasm, bilateral lens, bilateral optic nerve, bilateral eyeballs, bilateral parotid gland, spinal cord) delineated by MIM Atlas was statistically greater than that of Smart Segmentation (t=5.27, 4.41, 6.34, 5.70, 10.62, 7.45, 3.96, 4.26, 6.25, 5.42, 7.23, P<0.05). The average HD of 10 organs (brain stem, optic chiasm, bilateral lens, bilateral optic nerve, bilateral eyeballs, left parotid gland, spinal cord) delineated by MIM Atlas was statistically less than that of Smart Segmentation (t=-4.51, -4.49, -3.92, -3.45, -5.36, -5.56, -3.89, -3.90, -3.60, -3.68, P<0.05). The average △V of 6 organs (brain stem, optic chiasm, left len, bilateral optic nerve, right eyeball) delineated by MIM Atlas was statistically less than that of Smart Segmentation (t=-2.83, -3.39, -2.56, -2.27, -2.43, -2.51, P<0.05). Conclusions Both softwares have reasonable contouring accuracy for larger organs. The accuracy decreased with the decrease of organ volumes and blurred boundary. Generally, MIM Atlas′s performs better than Smart Segmentation does. Key words: Automatic contouring; Organs-at-risk segmentation; Atlas library; Nasopharyngeal carcinoma

A Novel Automatic Liver Segmentation by Level Set Method Over Real-Time Sensory Computed Tomography

The issues based on gastroenterology are the most distressing diseases in a human anatomy with the largest solid organ, liver that requires serious attention in its diagnosis. The challenging task of Liver segmentation is the main motive, proposed with the exploring of level set methodology with signed pressure force combined with masking and other Morphological operations. Next, the segmented liver multi-atlases are condensed together as a complete 3D projection of Human Liver in 360° shape angulation. The huge set of abdominal slices with 0.16 mm spacing of about 250 real time images of each subject, is treated as input with this proposed method and nearly 2550 CT images are used to get segmented using this methodology. The Evaluation Metrics given as 5 Error Metrics and 4 Performance Metrics obtained with the averaging of 10 atlases to each point, of the segmented atlases, outperforms the comparative methods of the Radiologist’s inference of liver boundary detection and an additional existing Local Fuzzy thresholding methodology. The overall visual outcome, evaluation metrical values and graphical data shows the peculiar performance of proposed methodology. This outcome with the electronic evolution of computed tomography appends to the wireless sensory waves to mark the CT images for our input. The doctors will be greatly helped by our process, to serve the global cause of saving lives by diagnosing accurately the complications of liver with appropriate segments, at early stage itself.

Clinical Evaluation of Commercial Atlas-Based Auto-Segmentation in the Head and Neck Region.

Background: While atlas segmentation (AS) has proven to be a time-saving and promising method for radiation therapy contouring, optimal methods for its use have not been well-established. Therefore, we investigated the relationship between the size of the atlas patient population and the atlas segmentation auto contouring (AC) performance.Methods: A total of 110 patients' head planning CT images were selected. The mandible and thyroid were selected for this study. The mandibles and thyroids of the patient population were carefully segmented by two skilled clinicians. Of the 110 patients, 100 random patients were registered to 5 different atlas libraries as atlas patients, in groups of 20 to 100, with increments of 20. AS was conducted for each of the remaining 10 patients, either by simultaneous atlas segmentation (SAS) or independent atlas segmentation (IAS). The AS duration of each target patient was recorded. To validate the accuracy of the generated contours, auto contours were compared to manually generated contours (MC) using a volume-overlap-dependent metric, Dice Similarity Coefficient (DSC), and a distance-dependent metric, Hausdorff Distance (HD).Results: In both organs, as the population increased from n = 20 to n = 60, the results showed better convergence. Generally, independent cases produced better performance than simultaneous cases. For the mandible, the best performance was achieved by n = 60 [DSC = 0.92 (0.01) and HD = 6.73 (1.31) mm] and the worst by n = 100 [DSC = 0.90 (0.03) and HD = 10.10 (6.52) mm] atlas libraries. Similar results were achieved with the thyroid; the best performance was achieved by n = 60 [DSC = 0.79 (0.06) and HD = 10.17 (2.89) mm] and the worst by n = 100 [DSC = 0.72 (0.13) and HD = 12.88 (3.94) mm] atlas libraries. Both IAS and SAS showed similar results. Manual contouring of the mandible and thyroid required an average of 1,044 (±170.15) seconds, while AS required an average of 46.4 (±2.8) seconds.Conclusions: The performance of AS AC generally increased as the population of the atlas library increased. However, the performance does not drastically vary in the larger atlas libraries in contrast to the logic that bigger atlas library should lead to better results. In fact, the results do not vary significantly toward the larger atlas library. It is necessary for the institutions to independently research the optimal number of subjects.