3 research outputs found
Automatic Response Assessment in Regions of Language Cortex in Epilepsy Patients Using ECoG-based Functional Mapping and Machine Learning
Accurate localization of brain regions responsible for language and cognitive
functions in Epilepsy patients should be carefully determined prior to surgery.
Electrocorticography (ECoG)-based Real Time Functional Mapping (RTFM) has been
shown to be a safer alternative to the electrical cortical stimulation mapping
(ESM), which is currently the clinical/gold standard. Conventional methods for
analyzing RTFM signals are based on statistical comparison of signal power at
certain frequency bands. Compared to gold standard (ESM), they have limited
accuracies when assessing channel responses.
In this study, we address the accuracy limitation of the current RTFM signal
estimation methods by analyzing the full frequency spectrum of the signal and
replacing signal power estimation methods with machine learning algorithms,
specifically random forest (RF), as a proof of concept. We train RF with power
spectral density of the time-series RTFM signal in supervised learning
framework where ground truth labels are obtained from the ESM. Results obtained
from RTFM of six adult patients in a strictly controlled experimental setup
reveal the state of the art detection accuracy of for the
language comprehension task, an improvement of over the conventional
RTFM estimation method. To the best of our knowledge, this is the first study
exploring the use of machine learning approaches for determining RTFM signal
characteristics, and using the whole-frequency band for better region
localization. Our results demonstrate the feasibility of machine learning based
RTFM signal analysis method over the full spectrum to be a clinical routine in
the near future.Comment: This paper will appear in the Proceedings of IEEE International
Conference on Systems, Man and Cybernetics (SMC) 201
Fully automatic cervical vertebrae segmentation framework for X-ray images
This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.The cervical spine is a highly flexible anatomy and therefore vulnerable to injuries. Unfortunately, a large number of injuries in lateral cervical X-ray images remain undiagnosed due to human errors. Computer-aided injury detection has the potential to reduce the risk of misdiagnosis. Towards building an automatic injury detection system, in this paper, we propose a deep learning-based fully automatic framework for segmentation of cervical vertebrae in X-ray images. The framework first localizes the spinal region in the image using a deep fully convolutional neural network. Then vertebra centers are localized using a novel deep probabilistic spatial regression network. Finally, a novel shape-aware deep segmentation network is used to segment the vertebrae in the image. The framework can take an X-ray image and produce a vertebrae segmentation result without any manual intervention. Each block of the fully automatic framework has been trained on a set of 124 X-ray images and tested on another 172 images, all collected from real-life hospital emergency rooms. A Dice similarity coefficient of 0.84 and a shape error of 1.69 mm have been achieved