Atlas-based Method Research Articles

Serial MRI studies over 12\u2009months using manual and atlas-based region of interest in patients with amyotrophic lateral sclerosis

BackgroundAmyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by loss of upper and lower motor neurons. There is a need for an imaging biomarker to track disease progression. Previously, magnetic resonance imaging (MRI) has shown loss of grey and white matter in the brain of patients with ALS compared to controls. We performed serial diffusion tractography imaging (DTI) study of patients with ALS looking for changes over time.MethodsOn all subjects (n = 15), we performed three MRI studies at 6 month intervals. DTI changes were assessed with tract-based spatial statistics (TBSS) and region of interest (ROI) studies. Cortic-spinal tract (CST) was selected for our ROI at the upper level; the posterior limb of internal capsule (PLIC), and a lower level in the pons.ResultsThere was no significant change in DTI measures over 12 months of observation. Better correlation of manual and atlas-based ROI methods was found in the posterior limb of the internal capsule than the pons.ConclusionWhile previous DTI studies showed significant differences between ALS subjects and controls, within individual subjects there is little evidence of progression over 12 months. This suggests that DTI is not a suitable biomarker to assess disease progression in ALS.

Almost 10\xa0years of PET/MR attenuation correction: the effect on lesion quantification with PSMA: clinical evaluation on 200 prostate cancer patients

PurposeAfter a decade of PET/MR, the case of attenuation correction (AC) remains open. The initial four-compartment (air, water, fat, soft tissue) Dixon-based AC scheme has since been expanded with several features, the latest being MR field-of-view extension and a bone atlas. As this potentially changes quantification, we evaluated the impact of these features in PET AC in prostate cancer patients.MethodsTwo hundred prostate cancer patients were examined with either 18F- or 68Ga-prostate-specific membrane antigen (PSMA) PET/MR. Qualitative and quantitative analysis (SUVmean, SUVmax, correlation, and statistical significance) was performed on images reconstructed using different AC schemes: Dixon, Dixon+MLAA, Dixon+HUGE, and Dixon+HUGE+bones for 18F-PSMA data; Dixon and Dixon+bones for 68Ga-PSMA data. Uptakes were compared using linear regression against standard Dixon.ResultsHigh correlation and no visually perceivable differences between all evaluated methods (r > 0.996) were found. The mean relative difference in lesion uptake of 18F-PSMA and 68Ga-PSMA remained, respectively, within 4% and 3% in soft tissue, and within 10% and 9% in bones for all evaluated methods. Bone registration errors were detected, causing mean uptake change of 5% in affected lesions.ConclusionsBased on these results and the encountered bone atlas registration inaccuracy, we deduce that including bones and extending the MR field-of-view did not introduce clinically significant differences in PSMA diagnostic accuracy and tracer uptake quantification in prostate cancer pelvic lesions, facilitating the analysis of serial studies respectively. However, in the absence of ground truth data, we advise against atlas-based methods when comparing serial scans for bone lesions.

Altered asymmetries of diffusion and volumetry in basal ganglia of schizophrenia.

Basal ganglia, which include the striatum and thalamus, have key roles in motivation, emotion, motor function, also contribute to higher-order cognitive function. Previous researches have documented structural and functional alterations in basal ganglia in schizophrenia. While few studies have assessed asymmetries of these characters in basal ganglia of schizophrenia. The current study investigated this issue by using diffusion tensor imaging, anatomic T1-weight image and resting-state functional data from 88 chronic schizophrenic subjects and 92 healthy controls. The structural characteristic, including fractional anisotropy, mean diffusivity (MD) and volume, were extracted and quantified from the subregions of basal ganglia, including caudate, putamen, pallidum and thalamus, through automated atlas-based method. The resting-state functional maps of these regions were also calculated through seed-based functional connectivity. Then, the laterality indexes of structural and functional features were calculated. Compared with healthy controls, schizophrenic subjects showed increased left laterality of volume in striatum and reduced left laterality of volume in thalamus. Furthermore, the difference of laterality of subregions in thalamus is compensatory in schizophrenic subjects. Importantly, the severity of patients' positive symptom was negative corelated with reduced left laterality of volume in thalamus. Our findings provide preliminary evidence demonstrating that the possibility of aberrant laterality in neural pathways and connectivity patterns related to the basal ganglia in schizophrenia.

Atlas-based liver segmentation for nonhuman primate research.

Certain viral infectious diseases cause systemic damage and the liver is an important organ affected directly by the virus and/or the hosts' response to the virus. Medical imaging indicates that the liver damage is heterogenous, and therefore, quantification of these changes requires analysis of the entire organ. Delineating the liver in preclinical imaging studies is a time-consuming and difficult task that would benefit from automated liver segmentation. A nonhuman primate atlas-based liver segmentation method was developed to support quantitative image analysis of preclinical research. A set of 82 computed tomography (CT) scans of nonhuman primates with associated manual contours delineating the liver was generated from normal and abnormal livers. The proposed technique uses rigid and deformable registrations, a majority vote algorithm, and image post-processing operations to automate the liver segmentation process. This technique was evaluated using Dice similarity, Hausdorff distance measures, and Bland-Altman plots. Automated segmentation results compare favorably with manual contouring, achieving a median Dice score of 0.91. Limits of agreement from Bland-Altman plots indicate that liver changes of 3 Hounsfield units (CT) and 0.4 SUVmean (positron emission tomography) are detectable using our automated method of segmentation, which are substantially less than changes observed in the host response to these viral infectious diseases. The proposed atlas-based liver segmentation technique is generalizable to various sizes and species of nonhuman primates and facilitates preclinical infectious disease research studies. While the image analysis software used is commercially available and facilities with funding can access the software to perform similar nonhuman primate liver quantitative analyses, the approach can be implemented in open-source frameworks as there is nothing proprietary about these methods.

A Multi-atlas Approach for Active Bone Marrow Sparing Radiation Therapy: Implementation in the NRG-GY006 Trial

Sparing active bone marrow (ABM) can reduce acute hematologic toxicity in patients undergoing chemoradiotherapy for cervical cancer, but ABM segmentation based on positron emission tomography/computed tomography (PET/CT) is costly. We sought to develop an atlas-based ABM segmentation method for implementation in a prospective clinical trial. A multiatlas was built on a training set of 144 patients and validated in 32 patients from the NRG-GY006 clinical trial. ABM for individual patients was defined as the subvolume of pelvic bone greater than the individual mean standardized uptake value on registered 18F-fluorodeoxyglucose PET/CT images. Atlas-based and custom ABM segmentations were compared using the Dice similarity coefficient and mean distance to agreement and used to generate ABM-sparing intensity modulated radiation therapy plans. Dose-volume metrics and normal tissue complication probabilities of the two approaches were compared using linear regression. Atlas-based ABM volumes (mean [standard deviation], 548.4 [88.3] cm3) were slightly larger than custom ABM volumes (535.1 [93.2] cm3), with a Dice similarity coefficient of 0.73. Total pelvic bone marrow V20 and Dmean were systematically higher and custom ABM V10 was systematically lower with custom-based plans (slope: 1.021 [95% confidence interval (CI), 1.005-1.037], 1.014 [95% CI, 1.006-1.022], and 0.98 [95% CI, 0.97-0.99], respectively). We found no significant differences between atlas-based and custom-based plans in bowel, rectum, bladder, femoral heads, or target dose-volume metrics. Atlas-based ABM segmentation can reduce pelvic bone marrow dose while achieving comparable targetandother normal tissue dosimetry. This approach may allow ABM sparing in settings where PET/CT is unavailable.

Frontal aslant tracts as correlates of lexical retrieval in MS

ABSTRACT Introduction Previous studies reveal that a newly described white matter pathway, the frontal aslant tract (FAT), connecting inferior and superior frontal gyri has a role in speech and language functions. We explored the role of this tract in a phonemic and semantic fluency tasks in a cohort of multiple sclerosis patients diagnosed with cognitive impairment. Methods Thirty-five MS patients with varying degrees of cognitive impairment underwent diffusion tensor imaging and the Controlled Associated Word Test. Fractional anisotropy (FA) of FAT and arcuate fasciculus (AF) were obtained through a supervised, atlas-based tissue segmentation and parcellation method. Phonemic and semantic fluency scores were obtained from COWAT. We ran a multivariate regression model, and partial correlation analyses adjusted for age, education, and lesion load, and corrected for multiple comparisons. False discovery rate (FDR) was used for the correction of multiple comparisons. Results Bilateral FAT FA showed significant association with phonemic verbal fluency task (Left; r = 0.46, p = 0.0058 and right; r = 0.46, p = 0.0059) but not semantic fluency task and this relation remained significant after FDR correction (p = 0.02 bilaterally). Although left AF showed some significant association with phonemic fluency task, this relation was insignificant after FDR correction. Conclusion We show that bilateral FAT are correlates of phonemic verbal fluency task but not semantic in an MS cohort with cognitive impairment. This finding suggests that FAT is more specialized in lexical retrieval function as semantic fluency test encompasses all the functions except the lexical retrieval.

Regional SUV quantification in hybrid PET/MR, a comparison of two atlas-based automatic brain segmentation methods

BackgroundQuantitative analysis of brain positron-emission tomography (PET) depends on structural segmentation, which can be time-consuming and operator-dependent when performed manually. Previous automatic segmentation usually registered subjects’ images onto an atlas template (defined as RSIAT here) for group analysis, which changed the individuals’ images and probably affected regional PET segmentation. In contrast, we could register atlas template to subjects’ images (RATSI), which created an individual atlas template and may be more accurate for PET segmentation. We segmented two representative brain areas in twenty Parkinson disease (PD) and eight multiple system atrophy (MSA) patients performed in hybrid positron-emission tomography/magnetic resonance imaging (PET/MR). The segmentation accuracy was evaluated using the Dice coefficient (DC) and Hausdorff distance (HD), and the standardized uptake value (SUV) measurements of these two automatic segmentation methods were compared, using manual segmentation as a reference.ResultsThe DC of RATSI increased, and the HD decreased significantly (P < 0.05) compared with the RSIAT in PD, while the results of one-way analysis of variance (ANOVA) found no significant differences in the SUVmean and SUVmax among the two automatic and the manual segmentation methods. Further, RATSI was used to compare regional differences in cerebral metabolism pattern between PD and MSA patients. The SUVmean in the segmented cerebellar gray matter for the MSA group was significantly lower compared with the PD group (P < 0.05), which is consistent with previous reports.ConclusionThe RATSI was more accurate for the caudate nucleus and putamen automatic segmentation and can be used for regional PET analysis in hybrid PET/MR.

Impacts of skull stripping on construction of three-dimensional T1-weighted imaging-based brain structural network in full-term neonates

BackgroundSkull stripping remains a challenge for neonatal brain MR image analysis. However, little is known about the accuracy of how skull stripping affects the neonatal brain tissue segmentation and subsequent network construction. This paper therefore aimed to clarify this issue by comparing two automatic (FMRIB Software Library’s Brain Extraction Tool, BET; Infant Brain Extraction and Analysis Toolbox, iBEAT) and a semiautomatic (iBEAT with manual correction) processes in constructing 3D T1-weighted imaging (T1WI)-based brain structural network.MethodsTwenty-two full-term neonates (gestational age, 37–42 weeks; boys/girls, 13/9) without abnormalities on MRI who underwent brain 3D T1WI were retrospectively recruited. Two automatic (BET and iBEAT) and a semiautomatic preprocessing (iBEAT with manual correction) workflows were separately used to perform the skull stripping. Brain tissue segmentation and volume calculation were performed by a Johns Hopkins atlas-based method. Sixty-four gray matter regions were selected as nodes; volume covariance network and its properties (clustering coefficient, Cp; characteristic path length, Lp; local efficiency, Elocal; global efficiency, Eglobal) were calculated by GRETNA. Analysis of variance (ANOVA) was used to compare the differences in the calculated volume between three workflows.ResultsThere were significant differences in volumes of 50 brain regions between the three workflows (P < 0.05). Three neonatal brain structural networks presented small-world topology. The semiautomatic workflow showed remarkably decreased Cp, increased Lp, decreased Elocal, and decreased Eglobal, in contrast to the two automatic ones.ConclusionsImperfect skull stripping indeed affected the accuracy of brain structural network in full-term neonates.

Segmentation of prostate zones using probabilistic atlas-based method with diffusion-weighted MR images

Background and objectiveAccurate segmentation of prostate and its zones constitute an essential preprocessing step for computer-aided diagnosis and detection system for prostate cancer (PCa) using diffusion-weighted imaging (DWI). However, low signal-to-noise ratio and high variability of prostate anatomic structures are challenging for its segmentation using DWI. We propose a semi-automated framework that segments the prostate gland and its zones simultaneously using DWI. MethodsIn this paper, the Chan-Vese active contour model along with morphological opening operation was used for segmentation of prostate gland. Then segmentation of prostate zones into peripheral zone (PZ) and transition zone (TZ) was carried out using in-house developed probabilistic atlas with partial volume (PV) correction algorithm. The study cohort included MRI dataset of 18 patients (n = 18) as our dataset and methodology were also independently evaluated using 15 MRI scans (n = 15) of QIN-PROSTATE-Repeatability dataset. The atlas for zones of prostate gland was constructed using dataset of twelve patients of our patient cohort. Three-fold cross-validation was performed with 10 repetitions, thus total 30 instances of training and testing were performed on our dataset followed by independent testing on the QIN-PROSTATE-Repeatability dataset. Dice similarity coefficient (DSC), Jaccard coefficient (JC), and accuracy were used for quantitative assessment of the segmentation results with respect to boundaries delineated manually by an expert radiologist. A paired t-test was performed to evaluate the improvement in zonal segmentation performance with the proposed PV correction algorithm. ResultsFor our dataset, the proposed segmentation methodology produced improved segmentation with DSC of 90.76 ± 3.68%, JC of 83.00 ± 5.78%, and accuracy of 99.42 ± 0.36% for the prostate gland, DSC of 77.73 ± 2.76%, JC of 64.46 ± 3.43%, and accuracy of 82.47 ± 2.22% for the PZ, and DSC of 86.05 ± 1.50%, JC of 75.80 ± 2.10%, and accuracy of 91.67 ± 1.56% for the TZ. The segmentation performance for QIN-PROSTATE-Repeatability dataset was, DSC of 85.50 ± 4.43%, JC of 75.00 ± 6.34%, and accuracy of 81.52 ± 5.55% for prostate gland, DSC of 74.40 ± 1.79%, JC of 59.53 ± 8.70%, and accuracy of 80.91 ± 5.16% for PZ, and DSC of 85.80 ± 5.55%, JC of 74.87 ± 7.90%, and accuracy of 90.59 ± 3.74% for TZ. With the implementation of the PV correction algorithm, statistically significant (p<0.05) improvements were observed in all the metrics (DSC, JC, and accuracy) for both prostate zones, PZ and TZ segmentation. ConclusionsThe proposed segmentation methodology is stable, accurate, and easy to implement for segmentation of prostate gland and its zones (PZ and TZ). The atlas-based segmentation framework with PV correction algorithm can be incorporated into a computer-aided diagnostic system for PCa localization and treatment planning.

Optimum configuration design and sensitivity analysis of the 3-RRR PPMs with a general kinematic model

In this work, a parameterized model of 3-RRR planar parallel manipulator is proposed, aiming to optimize the overall performance with optimum shapes and sizes of the base and mobile platforms. In the model, the inverse and direct kinematics are addressed, upon which the global kinematic performance in terms of dexterity index is calculated and analyzed. A shape and dimensional optimization is accomplished through the atlas-based method. Optimum configurations are obtained from the global performance atlases. Moreover, sensitivity analysis of overall kinematic performance with respect to each configuration parameter of planar 3-RRR parallel manipulator is performed to reveal the influences of the design parameters. A case study is included to present a more detailed design of 3-RRR manipulator.

M149. THE TOPOGRAPHY OF STRIATAL DOPAMINE AND SYMPTOMS IN PSYCHOSIS: AN INTEGRATIVE PET AND MRI STUDY

BackgroundStriatal dopamine dysfunction is proposed to underlie symptoms in psychosis, yet it is not known how changes in a single neurotransmitter could underlie the heterogenous presentations that are seen clinically. One hypothesis is that the symptomatic consequences of aberrant dopamine signalling may depend on where within the striatum dysfunction occurs. Positron emission tomography (PET) allows for the measurement of dopamine function across the striatum. However, when using typical atlas-based parcellation methods, the collinearity between measures of dopamine function within each striatal subdivision precludes investigation of this hypothesis.MethodsWe use a novel and data-driven parcellation method to address the above, and investigate relationships between spatial variability in dopamine synthesis capacity and psychotic symptoms. We employ a multimodal imaging approach combining 18F-DOPA PET and resting state MRI in 29 unmedicated and minimally-treated patients with first episode psychosis and 21 healthy controls. In each participant, we use resting state functional connectivity maps to quantify the functional connectivity of each striatal voxel to cortical networks. Network-specific striatal dopamine synthesis capacity (Kicer) was calculated for the resulting connectivity defined parcellations.ResultsConnectivity defined parcellations generated Kicer values with equivalent reliability, and significantly greater orthogonality to standard anatomical parcellation methods. Dopamine function within striatal areas connected to the default mode network is strongly associated with negative symptoms (p<0.001).DiscussionThese findings suggest that individual differences in the topography of striatal dopamine dysfunction contribute to shaping psychotic symptomatology.

Generation of Pseudo-CT using High-Degree Polynomial Regression on Dual-Contrast Pelvic MRI Data

Increasing interests in using magnetic resonance imaging only in radiation therapy require methods for predicting the computed tomography numbers from MRI data. Here we propose a simple voxel method to generate the pseudo-CT (pCT) image using dual-contrast pelvic MRI data. The method is first trained with the CT data and dual-contrast MRI data (two sets of MRI with different sequences) of multiple patients, where the anatomical structures in the images after deformable image registration are segmented into several regions, and after MRI intensity normalizations a regression analysis is used to determine a two-variable polynomial function for each region to relate a voxel’s two MRI intensity values to its CT number. We first evaluate the accuracy via the Hounsfield unit (HU) difference between the pseudo-CT and reference-CT (rCT) images and obtain the average mean absolute error as 40.3 ± 2.9 HU from leave-one-out-cross-validation (LOOCV) across all six patients, which is better than most previous results and comparable to another study using the more complicated atlas-based method. We also perform a dosimetric evaluation of the treatment plans based on pCT and rCT images and find the average passing rate within 2% dose difference to be 95.4% in point-to-point dose comparisons. Therefore, our method shows encouraging results in predicting the CT numbers. This polynomial method needs less computer storage than the interpolation method and can be readily extended to the case of more than two MRI sequences.

MR-based PET attenuation correction using a combined ultrashort echo time/multi-echo Dixon acquisition.

To develop a magnetic resonance (MR)-based method for estimation of continuous linear attenuation coefficients (LACs) in positron emission tomography (PET) using a physical compartmental model and ultrashort echo time (UTE)/multi-echo Dixon (mUTE) acquisitions. We propose a three-dimensional (3D) mUTE sequence to acquire signals from water, fat, and short T2 components (e.g., bones) simultaneously in a single acquisition. The proposed mUTE sequence integrates 3D UTE with multi-echo Dixon acquisitions and uses sparse radial trajectories to accelerate imaging speed. Errors in the radial k-space trajectories are measured using a special k-space trajectory mapping sequence and corrected for image reconstruction. A physical compartmental model is used to fit the measured multi-echo MR signals to obtain fractions of water, fat, and bone components for each voxel, which are then used to estimate the continuous LAC map for PET attenuation correction. The performance of the proposed method was evaluated via phantom and in vivo human studies, using LACs from computed tomography (CT) as reference. Compared to Dixon- and atlas-based MRAC methods, the proposed method yielded PET images with higher correlation and similarity in relation to the reference. The relative absolute errors of PET activity values reconstructed by the proposed method were below 5% in all of the four lobes (frontal, temporal, parietal, and occipital), cerebellum, whole white matter, and gray matter regions across all subjects (n=6). The proposed mUTE method can generate subject-specific, continuous LAC map for PET attenuation correction in PET/MR.

MRI-only Radiation Therapy: Pseudo-CT Based on Cubic-Feature Extraction and Alternative Regression Forest

Despite the extensive attention attracted by magnetic resonance imaging (MRI) in the radiation therapy, computed tomography was reintroduced by the researchers. During the calculation process of the 3D dose distribution of tissues, there were some arguments about the electron density information obtained from the CT scan. However, the CT-provided bones are accurate for constructing a radiograph. Recently, the advantages boosted by the soft tissue contrast relying on MRI and as well as the advantages boosted by CT imaging have been combined by the using of MRI/CT. Unfortunately, disadvantages still exist in the MRI/CT workflow because the voxel-intensities are unbalanced in the MRI and the CT scan. Here, based on the mapping method of CT and MRI, the potential of pseudo-CT (PCT) instead of CT planning was studied. The estimated PCT only from the corresponding MRI was obtained by using the patch-based random forest regression. The CT voxel target was trained by 3D Gabor feature in the MRI cube and the Local Binary Pattern (LBP). Besides, the regression task was solved by the alternative regression forest. According to the experiment, the method performs better than the current dictionary learning-based (DLB) method or atlas-based (AB) method.

Volume estimation of brain ventricles using Cavalieri’s principle and Atlas-based methods in Alzheimer disease: Consistency between methods

Automatic estimations of brain ventricles are needed to assess disease progression in neurodegenerative disorders such as Alzheimer Disease (AD). The objectives of this study are to evaluate the diagnostic performances of an automated volumetric assessment tool in estimating lateral ventricle volumes in AD and to compare this with Cavalieri’s principle, which is accepted as the gold standard method. This is across-sectional volumetric study including 25 Alzheimer patients and 25 healthy subjects undergoing magnetic resonance images (MRI) with a 3D turbo spin echo sequence at 1.5 Tesla. The Atlas-based method incorporated MRIStudio software to automatically measure he volumes of brain ventricles. To compare the corresponding measurements, we used manual point-counting and semi-automatic planimetry methods based on Cavalieri’s principle. Bland–Altman test results indicated an excellent agreement between Cavalieri’s principle and the Atlas-based method in all volumetric measurements (p < 0.05). We obtained a 64% sensitivity and 92% specificity for lateral ventricular volumes according to the Atlas-based method. AD subjects had significantly larger left and right lateral ventricle volume (LVV) when compared to control subjects in respect to three volumetric methods (p < 0.01). Lateral ventricle-to-brain ratio (VBR) statistically increased 49.23% in measurements done with the point-counting method, 45.12% with the planimetry method, and 45.49% with the Atlas-based method in AD patients (p < 0.01). As a result, the Atlas-based method may be used instead of manual volumetry to estimate brain volumes. Additionally, this method provides rapid and accurate estimations of brain ventricular volumes in-vivo examination of MRI.

Abdominal multi-organ auto-segmentation using 3D-patch-based deep convolutional neural network

Segmentation of normal organs is a critical and time-consuming process in radiotherapy. Auto-segmentation of abdominal organs has been made possible by the advent of the convolutional neural network. We utilized the U-Net, a 3D-patch-based convolutional neural network, and added graph-cut algorithm-based post-processing. The inputs were 3D-patch-based CT images consisting of 64 × 64 × 64 voxels designed to produce 3D multi-label semantic images representing the liver, stomach, duodenum, and right/left kidneys. The datasets for training, validating, and testing consisted of 80, 20, and 20 CT simulation scans, respectively. For accuracy assessment, the predicted structures were compared with those produced from the atlas-based method and inter-observer segmentation using the Dice similarity coefficient, Hausdorff distance, and mean surface distance. The efficiency was quantified by measuring the time elapsed for segmentation with or without automation using the U-Net. The U-Net-based auto-segmentation outperformed the atlas-based auto-segmentation in all abdominal structures, and showed comparable results to the inter-observer segmentations especially for liver and kidney. The average segmentation time without automation was 22.6 minutes, which was reduced to 7.1 minutes with automation using the U-Net. Our proposed auto-segmentation framework using the 3D-patch-based U-Net for abdominal multi-organs demonstrated potential clinical usefulness in terms of accuracy and time-efficiency.

A comparison of indirect and direct targeted STN DBS in the treatment of Parkinson\u2019s disease\u2014surgical method and clinical outcome over 15-year timespan

BackgroundDeep brain stimulation (DBS) in the subthalamic nucleus (STN) is used in advanced Parkinson’s disease (PD) for reducing motor fluctuations and the side effects of antiparkinsonian medication (APM). The development of neuroimaging has enabled the direct targeting of the STN. The aim of this study is to evaluate the outcome in patients with PD using STN DBS when changing from atlas-based indirect targeting method (iTM) to direct MRI-based targeting (dTM) assuming dTM is superior.MethodsTwenty-five consecutive PD patients underwent dTM STN DBS surgery from 2014 to 2017 with follow-up for 1 year. The neuroimaging, surgical method, outcome in Unified Parkinson’s Disease Rating Scale (UPDRS) scores, and reduction of APM are described and compared with the results of an earlier iTM STN DBS study.ResultsTwelve months after a dTM STN DBS, significant improvement (p < 0.001) was seen in six out of seven parameters of UPDRS when patients had medication (medON) and stimulation (stimON). The activities of daily living (UPDRSII) and motor scores (UPDRSIII) improved by 41% and 62%, respectively. Dyskinesias and fluctuations were both reduced by 81%. In dTM STN DBS group, the levodopa equivalent dose (LED) and the total daily levodopa equivalent dose (LEDD) were significantly decreased by 62% and 55%, respectively, compared with the baseline (p < 0.001). Five patients (20%) were without levodopa medication 12 months after the operation.ConclusionsThe development of surgical technique based on advanced neuroimaging has improved the outcome of PD STN DBS.

FCN Based Label Correction for Multi-Atlas Guided Organ Segmentation.

Segmentation of medical images using multiple atlases has recently gained immense attention due to their augmented robustness against variabilities across different subjects. These atlas-based methods typically comprise of three steps: atlas selection, image registration, and finally label fusion. Image registration is one of the core steps in this process, accuracy of which directly affects the final labeling performance. However, due to inter-subject anatomical variations, registration errors are inevitable. The aim of this paper is to develop a deep learning-based confidence estimation method to alleviate the potential effects of registration errors. We first propose a fully convolutional network (FCN) with residual connections to learn the relationship between the image patch pair (i.e., patches from the target subject and the atlas) and the related label confidence patch. With the obtained label confidence patch, we can identify the potential errors in the warped atlas labels and correct them. Then, we use two label fusion methods to fuse the corrected atlas labels. The proposed methods are validated on a publicly available dataset for hippocampus segmentation. Experimental results demonstrate that our proposed methods outperform the state-of-the-art segmentation methods.

Intraoperative Brain Shift Estimation Using Atlas of Brain Deformations and Constrained Kalman Filter

Intraoperative brain shift decreases the accuracy of neuronavigation systems based on preoperative images. In this paper, this problem is addressed by calculating an estimation of brain shift which can be employed to update the preoperative brain images. Therefore, the precision of navigation can be improved. In this regard, a brain shift estimation method is proposed using an atlas of brain deformations and constrained Kalman filter (ACKF). In addition, it is proven that the obtained ACKF estimation is the best unbiased minimax estimation when the risk function is the estimation error variance. Furthermore, a comparison is performed between the ACKF and two existing methods, namely, CKF and atlas-based method. The comparison demonstrates that the ACKF results in a more accurate estimation and needs less computation time. Finally, the supremacy of the proposed ACKF method with respect to the CKF and atlas-based method is illustrated through simulation.

Volumetric Segmentation of Brain Regions From MRI Scans Using 3D Convolutional Neural Networks

Automated brain segmentation is an active research domain due to the association of various neurological disorders with different regions of the brain, to help medical professionals in prognostics and diagnostics. Traditional techniques like atlas-based and pattern recognition-based methods led to the development of various tools for automated brain segmentation. Recently, deep learning techniques are outperforming classical state-of-the-art methods and gradually becoming more mature. Consequently, deep learning has been extensively employed as a tool for precise segmentation of brain regions because of its capability to learn the intricate features of the high-dimensional data. In this work, a network for the segmentation of multiple brain regions has been proposed that is based on 3D convolutional neural networks and utilizes residual learning and dilated convolution operations to efficiently learn the end-to-end mapping from MRI volumes to the voxel-level brain segments. This research is focused on the segmentation of up to nine brain regions including cerebrospinal fluid, white matter and gray matter as well as their sub-regions. Mean dice scores of 0.879 and 0.914 have been achieved for three and nine brain regions, respectively by using the data from three different sources. Comparative analysis shows that our network gives better dice scores for most of the brain regions than state-of-the-artwork. Moreover, the mean dice score of 0.903, obtained for eight brain regions segmentation with MRBrains18 dataset, is better than 0.876 which was achieved in the previous work.