This paper presents a new method for medical diagnosis of neurodegenerative diseases, such as Parkinson's, by extracting and using latent information from trained Deep convolutional, or convolutional-recurrent Neural Networks (DNNs). In particular, our approach adopts a combination of transfer learning, k-means clustering and k-Nearest Neighbour classification of deep neural network learned representations to provide enriched prediction of the disease based on MRI and or DaT Scan data. A new loss function is introduced and used in the training of the DNNs, so as to perform adaptation of the generated learned representations between data from different medical environments. Results are presented using a recently published database of Parkinson's related information, which was generated and evaluated in a hospital environment.
A Parkinson's database comprising MRI and DaT Scan data from 78 subjects, 55 patients with Parkinson's and 23 non patients, has been recently released @cite_14 ; it includes, in total 41528 MRI data (31147 from patients and 10381 from non patients) and 925 DaT scans (595 and 330 respectively). Our developments next are based on this database.
@cite_14
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Purpose: Repeated brain MRI scans are performed in many clinical scenarios, such as follow up of patients with tumors and therapy response assessment. In this paper, the authors show an approach to utilize former scans of the patient for the acceleration of repeated MRI scans. Methods: The proposed approach utilizes the possible similarity of the repeated scans in longitudinal MRI studies. Since similarity is not guaranteed, sampling and reconstruction are adjusted during acquisition to match the actual similarity between the scans. The baseline MR scan is utilized both in the sampling stage, via adaptive sampling, and in the reconstruction stage, with weighted reconstruction. In adaptive sampling, k-space sampling locations are optimized during acquisition. Weighted reconstruction uses the locations of the nonzero coefficients in the sparse domains as a prior in the recovery process. The approach was tested on 2D and 3D MRI scans of patients with brain tumors. Results: The longitudinal adaptive compressed sensing MRI (LACS-MRI) scheme provides reconstruction quality which outperforms other CS-based approaches for rapid MRI. Examples are shown on patients with brain tumors and demonstrate improved spatial resolution. Compared with data sampled at the Nyquist rate, LACS-MRI exhibits signal-to-error ratio (SER) of 24.8 dB with undersampling factor of 16.6 in 3D MRI. Conclusions: The authors presented an adaptive method for image reconstruction utilizing similarity of scans in longitudinal MRI studies, where possible. The proposed approach can significantly reduce scanning time in many applications that consist of disease follow-up and monitoring of longitudinal changes in brain MRI. Mobile phones with built-in accelerometers promise a convenient, objective way to quantify everyday movements and classify those movements into activities. Using accelerometer data we estimate the activities of 18 healthy subjects and 8 patients with Parkinson’s disease – e.g. walking, standing, sitting, or simply holding the phone. We use standard machine learning classifiers (support vector machines, regularized logistic regression) to automatically select, weigh, and combine a large set of standard features for time series analysis. Using crossvalidation across all samples we are able to correctly identify 96.1 of the activities of healthy subjects and 92.2 of the activities of Parkinson’s patients. However, when applying the classification parameters derived from the set of healthy subjects to Parkinson’s patients, the percent correct lowers to 60.3 , due to different characteristics of movement. For a fairer comparison across populations we also applied subject-wise crossvalidation, identifying healthy subject activities with 86.0 accuracy and 75.1 accuracy for patients. We discuss the key differences between these populations, and why algorithms designed for and trained with healthy subject data are not reliable for activity recognition in populations with motor disabilities. We present an adversarial domain adaptation based deep learning approach for automatic tumor segmentation from T2-weighted MRI. Our approach is composed of two steps: (i) a tumor-aware unsupervised cross-domain adaptation (CT to MRI), followed by (ii) semi-supervised tumor segmentation using Unet trained with synthesized and limited number of original MRIs. We introduced a novel target specific loss, called tumor-aware loss, for unsupervised cross-domain adaptation that helps to preserve tumors on synthesized MRIs produced from CT images. In comparison, state-of-the art adversarial networks trained without our tumor-aware loss produced MRIs with ill-preserved or missing tumors. All networks were trained using labeled CT images from 377 patients with non-small cell lung cancer obtained from the Cancer Imaging Archive and unlabeled T2w MRIs from a completely unrelated cohort of 6 patients with pre-treatment and 36 on-treatment scans. Next, we combined 6 labeled pre-treatment MRI scans with the synthesized MRIs to boost tumor segmentation accuracy through semi-supervised learning. Semi-supervised training of cycle-GAN produced a segmentation accuracy of 0.66 computed using Dice Score Coefficient (DSC). Our method trained with only synthesized MRIs produced an accuracy of 0.74 while the same method trained in semi-supervised setting produced the best accuracy of 0.80 on test. Our results show that tumor-aware adversarial domain adaptation helps to achieve reasonably accurate cancer segmentation from limited MRI data by leveraging large CT datasets. Pattern recognition methods using neuroimaging data for the diagnosis of Alzheimer's disease have been the subject of extensive research in recent years. In this paper, we use deep learning methods, and in particular sparse autoencoders and 3D convolutional neural networks, to build an algorithm that can predict the disease status of a patient, based on an MRI scan of the brain. We report on experiments using the ADNI data set involving 2,265 historical scans. We demonstrate that 3D convolutional neural networks outperform several other classifiers reported in the literature and produce state-of-art results. Alzheimer’s disease (AD) is characterized by neurofibrillary tangle and neuropil thread deposition, which ultimately results in neuronal loss. A large number of magnetic resonance imaging studies have reported a smaller hippocampus in AD patients as compared to healthy elderlies. Even though this difference is often interpreted as atrophy, it is only an indirect measurement. A more direct way of measuring the atrophy is to use repeated MRIs within the same individual. Even though several groups have used this appropriate approach, the pattern of hippocampal atrophy still remains unclear and difficult to relate to underlying pathophysiology. Here, in this longitudinal study, we aimed to map hippocampal atrophy rates in patients with AD, mild cognitive impairment (MCI) and elderly controls. Data consisted of two MRI scans for each subject. The symmetric deformation field between the first and the second MRI was computed and mapped onto the three-dimensional hippocampal surface. The pattern of atrophy rate was similar in all three groups, but the rate was significantly higher in patients with AD than in control subjects. We also found higher atrophy rates in progressive MCI patients as compared to stable MCI, particularly in the antero-lateral portion of the right hippocampus. Importantly, the regions showing the highest atrophy rate correspond to those that were described to have the highest burden of tau deposition. Our results show that local hippocampal atrophy rate is a reliable biomarker of disease stage and progression and could also be considered as a method to objectively evaluate treatment effects. Spleen volume estimation using automated image segmentation technique may be used to detect splenomegaly (abnormally enlarged spleen) on Magnetic Resonance Imaging (MRI) scans. In recent years, Deep Convolutional Neural Networks (DCNN) segmentation methods have demonstrated advantages for abdominal organ segmentation. However, variations in both size and shape of the spleen on MRI images may result in large false positive and false negative labeling when deploying DCNN based methods. In this paper, we propose the Splenomegaly Segmentation Network (SSNet) to address spatial variations when segmenting extraordinarily large spleens. SSNet was designed based on the framework of image-to-image conditional generative adversarial networks (cGAN). Specifically, the Global Convolutional Network (GCN) was used as the generator to reduce false negatives, while the Markovian discriminator (PatchGAN) was used to alleviate false positives. A cohort of clinically acquired 3D MRI scans (both T1 weighted and T2 weighted) from patients with splenomegaly were used to train and test the networks. The experimental results demonstrated that a mean Dice coefficient of 0.9260 and a median Dice coefficient of 0.9262 using SSNet on independently tested MRI volumes of patients with splenomegaly. Background Individual magnetic resonance imaging (MRI) disease severity measures, such as atrophy or lesions, show weak relationships to clinical status in patients with multiple sclerosis (MS). Objective To combine MS-MRI measures of disease severity into a composite score. Design Retrospective analysis of prospectively collected data. Setting Community-based and referral subspecialty clinic in an academic hospital. Patients A total of 103 patients with MS, with a mean (SD) Expanded Disability Status Scale (EDSS) score of 3.3 (2.2), of whom 62 (60.2 ) had the relapsing-remitting, 33 (32.0 ) the secondary progressive, and 8 (7.8 ) the primary progressive form. Main Outcome Measures Brain MRI measures included baseline T2 hyperintense (T2LV) and T1 hypointense (T1LV) lesion volume and brain parenchymal fraction (BPF), a marker of global atrophy. The ratio of T1LV to T2LV (T1:T2) assessed lesion severity. A Magnetic Resonance Disease Severity Scale (MRDSS) score, on a continuous scale from 0 to 10, was derived for each patient using T2LV, BPF, and T1:T2. Results The MRDSS score averaged 5.1 (SD, 2.6). Baseline MRI and EDSS correlations were moderate for BPF, T1:T2, and MRDSS and weak for T2LV. The MRDSS showed a larger effect size than the individual MRI components in distinguishing patients with the relapsing-remitting form from those with the secondary progressive form. Models containing either T2LV or MRDSS were significantly associated with disability progression during the mean (SD) 3.2 (0.3)–year observation period, when adjusting for baseline EDSS score. Conclusion Combining brain MRI lesion and atrophy measures can predict MS clinical progression and provides the basis for developing an MRI-based continuous scale as a marker of MS disease severity. This paper analyses gait patterns of patients with Parkinson;s Disease (PD) based on the acceleration data given by an e-AR sensor. Ten PD patients wearing the e-AR sensor walked along a 7m walkway and each session contained 16 repeated trials. An iterative algorithm has been proposed to produce robust estimations in the case of measurement noise and short-duration of gait signals. Step-frequency as a gait parameter derived from the estimated heel-contacts is calculated and validated using the CODA motion-capture system. Intersession variability of step-frequency for each patient and the overall variability across patients demonstrate a good agreement between estimations from the e-AR and CODA systems. Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi-center data set. Using clinical practice as the starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer's disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with a total of 29 algorithms. The algorithms were trained on a small training set (n=30) and optionally on data from other sources (e.g., the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0 and an area under the receiver-operating-characteristic curve (AUC) of 78.8 . In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework: http: caddementia.grand-challenge.org. White matter hyperintensities (WMH) are commonly seen in the brain of healthy elderly subjects and patients with several neurological and vascular disorders. A truly reliable and fully automated method for quantitative assessment of WMH on magnetic resonance imaging (MRI) has not yet been identified. In this paper, we review and compare the large number of automated approaches proposed for segmentation of WMH in the elderly and in patients with vascular risk factors. We conclude that, in order to avoid artifacts and exclude the several sources of bias that may influence the analysis, an optimal method should comprise a careful preprocessing of the images, be based on multimodal, complementary data, take into account spatial information about the lesions and correct for false positives. All these features should not exclude computational leanness and adaptability to available data. Patients with Parkinsons Disease (PD) frequently exhibit deficits in the production of emotional speech. In this paper, we examine the classification of emotional speech in patients with PD and the classification of PD speech. Participants were recorded speaking short statements with different emotional prosody which were classified with three methods (naive Bayes, random forests, and support vector machines) using 209 unique auditory features. Feature sets were reduced using simple statistical testing. We achieve accuracies of 65.5 and 73.33 on classifying between the emotions and between PD vs. control, respectively. These results may assist in the future development of automated early detection systems for diagnosing patients with PD. Availability of multi-modal magnetic resonance imaging (MRI) databases opens up the opportunity to synthesize different MRI contrasts without actually acquiring the images. In theory such synthetic images have the potential to reduce the amount of acquisitions to perform certain analyses. However, to what extent they can substitute real acquisitions in the respective analyses is an open question. In this study, we used a synthesis method based on patch matching to test whether synthetic images can be useful in segmentation and inter-modality cross-subject registration of brain MRI. Thirty-nine T1 scans with 36 manually labeled structures of interest were used in the registration and segmentation of eight proton density (PD) scans, for which ground truth T1 data were also available. The results show that synthesized T1 contrast can considerably enhance the quality of non-linear registration compared with using the original PD data, and it is only marginally worse than using the original T1 scans. In segmentation, the relative improvement with respect to using the PD is smaller, but still statistically significant. A significant proportion of patients scanned in a clinical setting have follow-up scans. We show in this work that such longitudinal scans alone can be used as a form of “free” self-supervision for training a deep network. We demonstrate this self-supervised learning for the case of T2-weighted sagittal lumbar Magnetic Resonance Images (MRIs). A Siamese convolutional neural network (CNN) is trained using two losses: (i) a contrastive loss on whether the scan is of the same person (i.e. longitudinal) or not, together with (ii) a classification loss on predicting the level of vertebral bodies. The performance of this pre-trained network is then assessed on a grading classification task. We experiment on a dataset of 1016 subjects, 423 possessing follow-up scans, with the end goal of learning the disc degeneration radiological gradings attached to the intervertebral discs. We show that the performance of the pre-trained CNN on the supervised classification task is (i) superior to that of a network trained from scratch; and (ii) requires far fewer annotated training samples to reach an equivalent performance to that of the network trained from scratch. Multiple sclerosis (MS) represents a spectrum of demyelination that depends on disease duration and clinical categorization. Most patients present with the relapsing–remitting form of the disease. The earliest clinical presentation of relapsing–remitting MS (RRMS) is the clinically isolated syndrome (CIS). Predicting which CIS patients are at high risk for MS is complicated by the disparity between clinical attacks and the extent of axon pathology. However, recent interferon-beta (IFN-β) trials have demonstrated a delay in time to the second demyelinating event with early treatment, and early treatment could also slow the progression from RRMS to secondary-progressive MS (SPMS). Clinical findings in combination with brain MRI and CSF analysis can be used in CIS patients to evaluate their risk for clinically definite MS (CDMS). Application of the McDonald criteria also allows an earlier MS diagnosis by using new MRI lesions to define dissemination in time. Early immunomodulatory therapy for selected CIS patients may eventually prevent future axon pathology and progression of disability in this lifelong disease. MRI has evolved into an important diagnostic technique in medical imaging. However, reliability of the derived diagnosis can be degraded by artifacts, which challenge both radiologists and automatic computer-aided diagnosis. This paper proposes a fully automatic method for measuring image quality of 3D structural MRI. Quality measures are derived by analyzing the air background of magnitude images and are capable of detecting image degradation from several sources, including bulk motion, residual magnetization from incomplete spoiling, blurring, ghosting, etc. The method has been validated on 749 3D T1-weighted 1.5 T and 3 T head scans acquired at 36 Alzheimer's Disease Neuroimaging Initiative (ADNI) study sites operating with various software and hardware combinations. Results are compared against qualitative grades assigned by the ADNI quality control center (taken as the reference standard). The derived quality indices are independent of the MRI system used and agree with the reference standard quality ratings with high sensitivity and specificity (>85 ). The proposed procedures for quality assessment could be of great value for both research and routine clinical imaging. It could greatly improve workflow through its ability to rule-out the need for a repeat scan while the patient is still in the magnet bore. Automatic food image recognition systems are alleviating the process of food-intake estimation and dietary assessment. However, due to the nature of food images, their recognition is a particularly challenging task, which is why traditional approaches in the field have achieved a low classification accuracy. Deep neural networks have outperformed such solutions, and we present a novel approach to the problem of food and drink image detection and recognition that uses a newly-defined deep convolutional neural network architecture, called NutriNet. This architecture was tuned on a recognition dataset containing 225,953 512 × 512 pixel images of 520 different food and drink items from a broad spectrum of food groups, on which we achieved a classification accuracy of 86.72 , along with an accuracy of 94.47 on a detection dataset containing 130,517 images. We also performed a real-world test on a dataset of self-acquired images, combined with images from Parkinson’s disease patients, all taken using a smartphone camera, achieving a top-five accuracy of 55 , which is an encouraging result for real-world images. Additionally, we tested NutriNet on the University of Milano-Bicocca 2016 (UNIMIB2016) food image dataset, on which we improved upon the provided baseline recognition result. An online training component was implemented to continually fine-tune the food and drink recognition model on new images. The model is being used in practice as part of a mobile app for the dietary assessment of Parkinson’s disease patients. Objective: To examine the accuracy and effectiveness of a novel virtual reality (VR) simulation tool in capturing Parkinson’s disease psychosis (PDP) Background: Despite its frequency, there remains confusion regarding diagnosing PDP. This may reflect knowledge gaps of its spectrum (i.e., illusions, hallucinations, delusions), overlapping non-motor issues (i.e., vivid dreams, dementia), failure of patients caregivers and healthcare providers (HCP) to discuss PDP, or an inability to “see what patients see” in visual hallucinations. With VR technology, one can simulate experiences not possible in “real life,” and these simulations may provide useful educational tools. Design methods: We developed a novel VR simulation of PDP using Oculus rift technology and descriptions by patients, caregivers, and HCP. The simulation, which included two hallucinatory scenarios, was presented at the 2015 AAN and Movement Disorder Society meetings. Viewers at the latter meeting completed an IRB-approved questionnaire, using a 5-point Likert-scale (“very unlikely” to “very likely”), to rate the simulation’s accuracy of capturing PDP symptoms and impact, effectiveness as a teaching tool, influence on medical practice, and side effects. Results: Questionnaires were completed by 502 602 (83.4[percnt]) viewers (56.8[percnt] Movement Disorder specialists, 40[percnt] Allied HCP Other). Both VR scenarios accurately (“likely very likely”) captured PDP symptoms reported by patients and caregivers (76.4[percnt], 67.6[percnt], respectively) and their impact on patients and caregivers (78.4[percnt], 66.2[percnt], respectively). The scenarios were “likely very likely” to be effective teaching tools for HCP (87.5[percnt]) and caregivers (90.8[percnt]), though slightly less for patients (76.1[percnt]). About 50[percnt] of respondents would “likely very likely” change their medical practice. Few experienced difficulties using the simulation, though mild motion sickness was occasionally reported. Conclusions: Using virtual technology, viewers were able to experience PDP first-hand. The PDP VR simulation has potential as an educational tool for not only HCP, but also caregivers and patients. Greater awareness of PDP may help reduce diagnostic confusion and associated stigma. Disclosure: Dr. Goldman has received personal compensation for activities with Acadia, Teva and Pfizer as a consultant and Advisory Board Member. Dr. Goldman has received research support from NIH, Michael J. Fox Foundation, Rush University Medical Center, Parkinson9 Dr. Stebbins has nothing to disclose. Dr. Fredericks has nothing to disclose. Dr. Upchurch has nothing to disclose. The combination of positron emission tomography (PET) and magnetic resonance imaging (MRI) offers unique possibilities. In this paper we aim to exploit the high spatial resolution of MRI to enhance the reconstruction of simultaneously acquired PET data. We propose a new prior to incorporate structural side information into a maximum a posteriori reconstruction. The new prior combines the strengths of previously proposed priors for the same problem: it is very efficient in guiding the reconstruction at edges available from the side information and it reduces locally to edge-preserving total variation in the degenerate case when no structural information is available. In addition, this prior is segmentation-free, convex and no a priori assumptions are made on the correlation of edge directions of the PET and MRI images. We present results for a simulated brain phantom and for real data acquired by the Siemens Biograph mMRR for a hardware phantom and a clinical scan. The results from simulations show that the new prior has a better trade-off between enhancing common anatomical boundaries and preserving unique features than several other priors. Moreover, it has a better mean absolute biasto- mean standard deviation trade-off and yields reconstructions with superior relative 2-error and structural similarity index. These findings are underpinned by the real data results from a hardware phantom and a clinical patient confirming that the new prior is capable of promoting well-defined anatomical boundaries. Volumetric change in glioblastoma multiforme (GBM) over time is a critical factor in treatment decisions. Typically, the tumor volume is computed on a slice-by-slice basis using MRI scans obtained at regular intervals. (3D)Slicer – a free platform for biomedical research – provides an alternative to this manual slice-by-slice segmentation process, which is significantly faster and requires less user interaction. In this study, 4 physicians segmented GBMs in 10 patients, once using the competitive region-growing based GrowCut segmentation module of Slicer, and once purely by drawing boundaries completely manually on a slice-by-slice basis. Furthermore, we provide a variability analysis for three physicians for 12 GBMs. The time required for GrowCut segmentation was on an average 61 of the time required for a pure manual segmentation. A comparison of Slicer-based segmentation with manual slice-by-slice segmentation resulted in a Dice Similarity Coefficient of 88.43 ± 5.23 and a Hausdorff Distance of 2.32 ± 5.23 mm. Summary Background Mortality rate, the possible factors affecting mortality and intubation in patients with acute exacerbation of chronic obstructive pulmonary diseases (COPD) and hypercapnic respiratory failure (RF) are yet unclear. Objective To identify the possible factors affecting mortality and intubation in COPD patients. Design A prospective study using data obtained over the first 24h of respiratory intensive care unit (RICU) admission. Consecutive admissions of 656 patients were monitored and 151 of them who had acute exacerbation of COPD and hypercapnic RF were enrolled. Setting University hospital, Department of Chest Diseases, RICU. Results Mean age was 65.1 years. The mean APACHE II score was 23.7. Eighty-seven patients (57.6 ) received mechanical ventilation (MV) via an endotracheal tube for more than 24h. Twenty-two patients received non-invasive ventilation (NIV). Fifty patients died (33.1 ) in hospital during the study period. The mortality rate was 52.9 in patients in need of MV. In the multivariate analysis, the need for intubation, inadequate metabolic compensation for respiratory acidosis, and low (=bad) Glasgow Coma Score (GCS) were determined as independent factors associated with mortality. The low GCS (OR: 0.61; CI: 0.48–0.78) and high APACHE II score (OR: 1.24; CI: 1.11–1.38) were determined as factors associated with intubation. Conclusion The most important predictors related to hospital mortality were the need for invasive ventilation and complications to MV. Adequate metabolic compensation for respiratory acidosis at admittance is associated with better survival. A high APACHE II score and loss of consciousness (low GCS) were independent predictors of a need to intubate patients.