Convolutional Neural Network for Stereotypical Motor Movement Detection in Autism

Autism Spectrum Disorders (ASDs) are often associated with specific atypical postural or motor behaviors, of which Stereotypical Motor Movements (SMMs) have a specific visibility. While the identification and the quantification of SMM patterns remain complex, its automation would provide support to accurate tuning of the intervention in the therapy of autism. Therefore, it is essential to develop automatic SMM detection systems in a real world setting, taking care of strong inter-subject and intra-subject variability. Wireless accelerometer sensing technology can provide a valid infrastructure for real-time SMM detection, however such variability remains a problem also for machine learning methods, in particular whenever handcrafted features extracted from accelerometer signal are considered. Here, we propose to employ the deep learning paradigm in order to learn discriminating features from multi-sensor accelerometer signals. Our results provide preliminary evidence that feature learning and transfer learning embedded in the deep architecture achieve higher accurate SMM detectors in longitudinal scenarios.Comment: Presented at 5th NIPS Workshop on Machine Learning and Interpretation in Neuroimaging (MLINI), 2015, (http://arxiv.org/html/1605.04435), Report-no: MLINI/2015/1

Hybrid Deep Neural Network for Brachial Plexus Nerve Segmentation in Ultrasound Images

Ultrasound-guided regional anesthesia (UGRA) can replace general anesthesia (GA), improving pain control and recovery time. This method can be applied on the brachial plexus (BP) after clavicular surgeries. However, identification of the BP from ultrasound (US) images is difficult, even for trained professionals. To address this problem, convolutional neural networks (CNNs) and more advanced deep neural networks (DNNs) can be used for identification and segmentation of the BP nerve region. In this paper, we propose a hybrid model consisting of a classification model followed by a segmentation model to segment BP nerve regions in ultrasound images. A CNN model is employed as a classifier to precisely select the images with the BP region. Then, a U-net or M-net model is used for the segmentation. Our experimental results indicate that the proposed hybrid model significantly improves the segmentation performance over a single segmentation model.Comment: The first two authors contributed equall

HNT-AI:An Automatic Segmentation Framework for Head and Neck Primary Tumors and Lymph Nodes in FDG- PET/CT Images

Head and neck cancer is one of the most prevalent cancers in the world. Automatic delineation of primary tumors and lymph nodes is important for cancer diagnosis and treatment. In this paper, we develop a deep learning-based model for automatic tumor segmentation, HNT-AI, using PET/CT images provided by the MICCAI 2022 Head and Neck Tumor (HECKTOR) segmentation Challenge. We investigate the effect of residual blocks, squeeze-and-excitation normalization, and grid-attention gates on the performance of 3D-UNET. We project the predicted masks on the z-axis and apply k-means clustering to reduce the number of false positive predictions. Our proposed HNT-AI segmentation framework achieves an aggregated dice score of 0.774 and 0.759 for primary tumors and lymph nodes, respectively, on the unseen external test set. Qualitative analysis of the predicted segmentation masks shows that the predicted segmentation mask tends to follow the high standardized uptake value (SUV) area on the PET scans more closely than the ground truth masks. The largest tumor volume, the larget lymph node volume, and the total number of lymph nodes derived from the segmentation proved to be potential biomarkers for recurrence-free survival with a C-index of 0.627 on the test set

Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge

Consumer wearables and sensors are a rich source of data about patients' daily disease and symptom burden, particularly in the case of movement disorders like Parkinson's disease (PD). However, interpreting these complex data into so-called digital biomarkers requires complicated analytical approaches, and validating these biomarkers requires sufficient data and unbiased evaluation methods. Here we describe the use of crowdsourcing to specifically evaluate and benchmark features derived from accelerometer and gyroscope data in two different datasets to predict the presence of PD and severity of three PD symptoms: tremor, dyskinesia, and bradykinesia. Forty teams from around the world submitted features, and achieved drastically improved predictive performance for PD status (best AUROC = 0.87), as well as tremor- (best AUPR = 0.75), dyskinesia- (best AUPR = 0.48) and bradykinesia-severity (best AUPR = 0.95)

Deep Learning for Abnormal Movement Detection using Wearable Sensors: Case Studies on Stereotypical Motor Movements in Autism and Freezing of Gait in Parkinson's Disease

Inertial measurement sensing technology with the capability of capturing disease-relevant data has a great potential for improving the current clinical assessments and enhancing the quality of life in patients with neuro-developmental and neuro-degenerative diseases such as autism spectrum disorders (ASD) and Parkinson's disease (PD). The current clinical assessments can be improved by developing objective tools for the disease diagnosis and continuous monitoring of patients in out of clinical settings. To this end, it is necessary to develop automatic abnormal movement detection methods with the capability of adjusting on new patients' data in real-life settings. However, achieving this goal is challenging mainly because of the inter and intra-subject variability in acquired signals and the lack of labeled data. The research presented in this thesis investigates the application of deep neural networks to address these challenges of abnormal movement detection using inertial measurement unit (IMU) sensors with case studies on stereotypical motor movements in ASD and freezing of gait in PD patients. In this direction, this thesis provides four main contributions: i) A convolutional neural network (CNN) architecture is proposed to learn discriminative features which are sufficiently robust to inter and intra-subject variability. It is further shown how the proposed CNN architecture can be used for parameter transfer learning to enhance the adaptability of the abnormal movement detection system to new data in a longitudinal study. ii) An application of recurrent neural networks and more specifically long short-term memory (LSTM) in combination with CNN is proposed in order to incorporate more the temporal dynamics of IMU signals in the process of feature learning for abnormal movement detection. iii) An ensemble learning approach is proposed to improve the detection accuracy and at the same time to reduce the variance of models. iv) In the normative modeling framework, the problem of abnormal movement detection is redefined in the context of novelty detection and it is shown how a probabilistic denoising autoencoder can be used to learn the distribution of the normal human movements. The resulting deep normative model then is used in a novelty detection setting for unsupervised abnormal movement detection. The experimental results on three benchmark datasets collected from ASD and PD patients illustrate the high potentials of deep learning paradigm to address the crucial challenges toward real-time abnormal movement detection systems using wearable technologies

Novelty Detection using Deep Normative Modeling for IMU-Based Abnormal Movement Monitoring in Parkinson's Disease and Autism Spectrum Disorders

Contains fulltext : 197897.pdf (publisher's version ) (Open Access

Advanced Ultrasound and Photoacoustic Imaging in Cardiology

Cardiovascular diseases (CVDs) remain the leading cause of death worldwide. An effective management and treatment of CVDs highly relies on accurate diagnosis of the disease. As the most common imaging technique for clinical diagnosis of the CVDs, US imaging has been intensively explored. Especially with the introduction of deep learning (DL) techniques, US imaging has advanced tremendously in recent years. Photoacoustic imaging (PAI) is one of the most promising new imaging methods in addition to the existing clinical imaging methods. It can characterize different tissue compositions based on optical absorption contrast and thus can assess the functionality of the tissue. This paper reviews some major technological developments in both US (combined with deep learning techniques) and PA imaging in the application of diagnosis of CVDs

Stereotypical Motor Movement Detection in Dynamic Feature Space

Stereotypical Motor Movements (SMMs) are abnormal postural or motor behaviors that interfere with learning and social interaction in Autism Spectrum Disorder patients. An automatic SMM detection system, employing inertial sensing technology, provides a useful tool for real-time alert on the onset of these atypical behaviors, therefore facilitating personalized intervention therapies. To tackle critical issues with inter-subject variability, in this study, we propose to combine long short-term memory (LSTM) with convolutional neural network (CNN) to model the temporal patterns in the sequence of multi-axes IMU signals. Our results, on one simulated and two experimental datasets, show that transferring the raw feature space to a dynamic feature space via the proposed architecture enhances the performance of automatic SMM detection system especially for skewed training data. These findings facilitate the application of SMM detection system in real-time scenarios