Pub Date : 2023-12-18DOI: 10.1007/978-3-031-20716-7_2
T. Saquib, Demetri Terzopoulos
{"title":"Biomimetic Oculomotor Control with Spiking Neural Networks","authors":"T. Saquib, Demetri Terzopoulos","doi":"10.1007/978-3-031-20716-7_2","DOIUrl":"https://doi.org/10.1007/978-3-031-20716-7_2","url":null,"abstract":"","PeriodicalId":91444,"journal":{"name":"Advances in visual computing : ... international symposium, ISVC ... : proceedings. International Symposium on Visual Computing","volume":"1 1","pages":"1-14"},"PeriodicalIF":0.0,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89744266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-30DOI: 10.48550/arXiv.2210.16776
Veysel Kocaman, O. M. Shir, Thomas Bäck, A. Belbachir
We propose an augmentation policy for Contrastive Self-Supervised Learning (SSL) in the form of an already established Salient Image Segmentation technique entitled Global Contrast based Salient Region Detection. This detection technique, which had been devised for unrelated Computer Vision tasks, was empirically observed to play the role of an augmentation facilitator within the SSL protocol. This observation is rooted in our practical attempts to learn, by SSL-fashion, aerial imagery of solar panels, which exhibit challenging bound-ary patterns. Upon the successful integration of this technique on our problem domain, we formulated a generalized procedure and conducted a comprehensive, systematic performance assessment with various Contrastive SSL algorithms subject to standard augmentation techniques. This evaluation, which was conducted across multiple datasets, indicated that the proposed technique indeed contributes to SSL. We hypothesize whether salient image segmentation may suffice as the only augmentation policy in Contrastive SSL when treating downstream segmentation tasks.
{"title":"Saliency Can Be All You Need In Contrastive Self-Supervised Learning","authors":"Veysel Kocaman, O. M. Shir, Thomas Bäck, A. Belbachir","doi":"10.48550/arXiv.2210.16776","DOIUrl":"https://doi.org/10.48550/arXiv.2210.16776","url":null,"abstract":"We propose an augmentation policy for Contrastive Self-Supervised Learning (SSL) in the form of an already established Salient Image Segmentation technique entitled Global Contrast based Salient Region Detection. This detection technique, which had been devised for unrelated Computer Vision tasks, was empirically observed to play the role of an augmentation facilitator within the SSL protocol. This observation is rooted in our practical attempts to learn, by SSL-fashion, aerial imagery of solar panels, which exhibit challenging bound-ary patterns. Upon the successful integration of this technique on our problem domain, we formulated a generalized procedure and conducted a comprehensive, systematic performance assessment with various Contrastive SSL algorithms subject to standard augmentation techniques. This evaluation, which was conducted across multiple datasets, indicated that the proposed technique indeed contributes to SSL. We hypothesize whether salient image segmentation may suffice as the only augmentation policy in Contrastive SSL when treating downstream segmentation tasks.","PeriodicalId":91444,"journal":{"name":"Advances in visual computing : ... international symposium, ISVC ... : proceedings. International Symposium on Visual Computing","volume":"207 1","pages":"119-140"},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78774374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-25DOI: 10.48550/arXiv.2210.14349
Menghe Zhang, Weichen Liu, Nadir Weibel, J. Schulze
Preoperative medical imaging is an essential part of surgical planning. The data from medical imaging devices, such as CT and MRI scanners, consist of stacks of 2D images in DICOM format. Conversely, advances in 3D data visualization provide further information by assembling cross-sections into 3D volumetric datasets. As Microsoft unveiled the HoloLens 2 (HL2), which is considered one of the best Mixed Reality (XR) headsets in the market, it promised to enhance visualization in 3D by providing an immersive experience to users. This paper introduces a prototype holographic XR DICOM Viewer for the 3D visualization of DICOM image sets on HL2 for surgical planning. We first developed a standalone graphical C++ engine using the native DirectX11 API and HLSL shaders. Based on that, the prototype further applies the OpenXR API for potential deployment on a wide range of devices from vendors across the XR spectrum. With native access to the device, our prototype unravels the limitation of hardware capabilities on HL2 for 3D volume rendering and interaction. Moreover, smartphones can act as input devices to provide another user interaction method by connecting to our server. In this paper, we present a holographic DICOM viewer for the HoloLens 2 and contribute (i) a prototype that renders the DICOM image stacks in real-time on HL2, (ii) three types of user interactions in XR, and (iii) a preliminary qualitative evaluation of our prototype.
{"title":"A DirectX-Based DICOM Viewer for Multi-User Surgical Planning in Augmented Reality","authors":"Menghe Zhang, Weichen Liu, Nadir Weibel, J. Schulze","doi":"10.48550/arXiv.2210.14349","DOIUrl":"https://doi.org/10.48550/arXiv.2210.14349","url":null,"abstract":"Preoperative medical imaging is an essential part of surgical planning. The data from medical imaging devices, such as CT and MRI scanners, consist of stacks of 2D images in DICOM format. Conversely, advances in 3D data visualization provide further information by assembling cross-sections into 3D volumetric datasets. As Microsoft unveiled the HoloLens 2 (HL2), which is considered one of the best Mixed Reality (XR) headsets in the market, it promised to enhance visualization in 3D by providing an immersive experience to users. This paper introduces a prototype holographic XR DICOM Viewer for the 3D visualization of DICOM image sets on HL2 for surgical planning. We first developed a standalone graphical C++ engine using the native DirectX11 API and HLSL shaders. Based on that, the prototype further applies the OpenXR API for potential deployment on a wide range of devices from vendors across the XR spectrum. With native access to the device, our prototype unravels the limitation of hardware capabilities on HL2 for 3D volume rendering and interaction. Moreover, smartphones can act as input devices to provide another user interaction method by connecting to our server. In this paper, we present a holographic DICOM viewer for the HoloLens 2 and contribute (i) a prototype that renders the DICOM image stacks in real-time on HL2, (ii) three types of user interactions in XR, and (iii) a preliminary qualitative evaluation of our prototype.","PeriodicalId":91444,"journal":{"name":"Advances in visual computing : ... international symposium, ISVC ... : proceedings. International Symposium on Visual Computing","volume":"39 1","pages":"217-228"},"PeriodicalIF":0.0,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87483317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-25DOI: 10.48550/arXiv.2210.13668
Prithul Sarker, Sushmita Sarker, G. Bebis, A. Tavakkoli
Deep learning has made a breakthrough in medical image segmentation in recent years due to its ability to extract high-level features without the need for prior knowledge. In this context, U-Net is one of the most advanced medical image segmentation models, with promising results in mammography. Despite its excellent overall performance in segmenting multimodal medical images, the traditional U-Net structure appears to be inadequate in various ways. There are certain U-Net design modifications, such as MultiResUNet, Connected-UNets, and AU-Net, that have improved overall performance in areas where the conventional U-Net architecture appears to be deficient. Following the success of UNet and its variants, we have presented two enhanced versions of the Connected-UNets architecture: ConnectedUNets+ and ConnectedUNets++. In ConnectedUNets+, we have replaced the simple skip connections of Connected-UNets architecture with residual skip connections, while in ConnectedUNets++, we have modified the encoder-decoder structure along with employing residual skip connections. We have evaluated our proposed architectures on two publicly available datasets, the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) and INbreast.
{"title":"ConnectedUNets++: Mass Segmentation from Whole Mammographic Images","authors":"Prithul Sarker, Sushmita Sarker, G. Bebis, A. Tavakkoli","doi":"10.48550/arXiv.2210.13668","DOIUrl":"https://doi.org/10.48550/arXiv.2210.13668","url":null,"abstract":"Deep learning has made a breakthrough in medical image segmentation in recent years due to its ability to extract high-level features without the need for prior knowledge. In this context, U-Net is one of the most advanced medical image segmentation models, with promising results in mammography. Despite its excellent overall performance in segmenting multimodal medical images, the traditional U-Net structure appears to be inadequate in various ways. There are certain U-Net design modifications, such as MultiResUNet, Connected-UNets, and AU-Net, that have improved overall performance in areas where the conventional U-Net architecture appears to be deficient. Following the success of UNet and its variants, we have presented two enhanced versions of the Connected-UNets architecture: ConnectedUNets+ and ConnectedUNets++. In ConnectedUNets+, we have replaced the simple skip connections of Connected-UNets architecture with residual skip connections, while in ConnectedUNets++, we have modified the encoder-decoder structure along with employing residual skip connections. We have evaluated our proposed architectures on two publicly available datasets, the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) and INbreast.","PeriodicalId":91444,"journal":{"name":"Advances in visual computing : ... international symposium, ISVC ... : proceedings. International Symposium on Visual Computing","volume":"11 1","pages":"419-430"},"PeriodicalIF":0.0,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81430091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-17DOI: 10.48550/arXiv.2210.09148
Ga'etan Landreau, M. Tamaazousti
Image-based 3D reconstruction has increasingly stunning results over the past few years with the latest improvements in computer vision and graphics. Geometry and topology are two fundamental concepts when dealing with 3D mesh structures. But the latest often remains a side issue in the 3D mesh-based reconstruction literature. Indeed, performing per-vertex elementary displacements over a 3D sphere mesh only impacts its geometry and leaves the topological structure unchanged and fixed. Whereas few attempts propose to update the geometry and the topology, all need to lean on costly 3D ground-truth to determine the faces/edges to prune. We present in this work a method that aims to refine the topology of any 3D mesh through a face-pruning strategy that extensively relies upon 2D alpha masks and camera pose information. Our solution leverages a differentiable renderer that renders each face as a 2D soft map. Its pixel intensity reflects the probability of being covered during the rendering process by such a face. Based on the 2D soft-masks available, our method is thus able to quickly highlight all the incorrectly rendered faces for a given viewpoint. Because our module is agnostic to the network that produces the 3D mesh, it can be easily plugged into any self-supervised image-based (either synthetic or natural) 3D reconstruction pipeline to get complex meshes with a non-spherical topology.
{"title":"Pruning-based Topology Refinement of 3D Mesh using a 2D Alpha Mask","authors":"Ga'etan Landreau, M. Tamaazousti","doi":"10.48550/arXiv.2210.09148","DOIUrl":"https://doi.org/10.48550/arXiv.2210.09148","url":null,"abstract":"Image-based 3D reconstruction has increasingly stunning results over the past few years with the latest improvements in computer vision and graphics. Geometry and topology are two fundamental concepts when dealing with 3D mesh structures. But the latest often remains a side issue in the 3D mesh-based reconstruction literature. Indeed, performing per-vertex elementary displacements over a 3D sphere mesh only impacts its geometry and leaves the topological structure unchanged and fixed. Whereas few attempts propose to update the geometry and the topology, all need to lean on costly 3D ground-truth to determine the faces/edges to prune. We present in this work a method that aims to refine the topology of any 3D mesh through a face-pruning strategy that extensively relies upon 2D alpha masks and camera pose information. Our solution leverages a differentiable renderer that renders each face as a 2D soft map. Its pixel intensity reflects the probability of being covered during the rendering process by such a face. Based on the 2D soft-masks available, our method is thus able to quickly highlight all the incorrectly rendered faces for a given viewpoint. Because our module is agnostic to the network that produces the 3D mesh, it can be easily plugged into any self-supervised image-based (either synthetic or natural) 3D reconstruction pipeline to get complex meshes with a non-spherical topology.","PeriodicalId":91444,"journal":{"name":"Advances in visual computing : ... international symposium, ISVC ... : proceedings. International Symposium on Visual Computing","volume":"14 1","pages":"404-415"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88416540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-14DOI: 10.48550/arXiv.2210.08080
S. Devkota, S. Pattanaik
Modern-day display systems demand high-quality rendering. However, rendering at higher resolution requires a large number of data samples and is computationally expensive. Recent advances in deep learning-based image and video super-resolution techniques motivate us to investigate such networks for high-fidelity upscaling of frames rendered at a lower resolution to a higher resolution. While our work focuses on super-resolution of medical volume visualization performed with direct volume rendering, it is also applicable for volume visualization with other rendering techniques. We propose a learning-based technique where our proposed system uses color information along with other supplementary features gathered from our volume renderer to learn efficient upscaling of a low-resolution rendering to a higher-resolution space. Furthermore, to improve temporal stability, we also implement the temporal reprojection technique for accumulating history samples in volumetric rendering.
{"title":"Deep Learning based Super-Resolution for Medical Volume Visualization with Direct Volume Rendering","authors":"S. Devkota, S. Pattanaik","doi":"10.48550/arXiv.2210.08080","DOIUrl":"https://doi.org/10.48550/arXiv.2210.08080","url":null,"abstract":"Modern-day display systems demand high-quality rendering. However, rendering at higher resolution requires a large number of data samples and is computationally expensive. Recent advances in deep learning-based image and video super-resolution techniques motivate us to investigate such networks for high-fidelity upscaling of frames rendered at a lower resolution to a higher resolution. While our work focuses on super-resolution of medical volume visualization performed with direct volume rendering, it is also applicable for volume visualization with other rendering techniques. We propose a learning-based technique where our proposed system uses color information along with other supplementary features gathered from our volume renderer to learn efficient upscaling of a low-resolution rendering to a higher-resolution space. Furthermore, to improve temporal stability, we also implement the temporal reprojection technique for accumulating history samples in volumetric rendering.","PeriodicalId":91444,"journal":{"name":"Advances in visual computing : ... international symposium, ISVC ... : proceedings. International Symposium on Visual Computing","volume":"89 1","pages":"103-114"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80252632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-13DOI: 10.48550/arXiv.2210.09295
Khondker Fariha Hossain, Sharif Amit Kamran, Prithul Sarker, Philip Pavilionis, I. Adhanom, N. Murray, A. Tavakkoli
. Sport-related concussion (SRC) depends on sensory information from visual, vestibular, and somatosensory systems. At the same time, the current clinical administration of Vestibular/Ocular Motor Screening (VOMS) is subjective and deviates among administrators. Therefore, for the assessment and manage-ment of concussion detection, standardization is required to lower the risk of injury and increase the validation among clinicians. With the advancement of technology, virtual reality (VR) can be utilized to advance the standardization of the VOMS, increasing the accuracy of testing administration and decreasing overall false positive rates. In this paper, we experimented with multiple machine learning methods to detect SRC on VR-generated data using VOMS. In our observation, the data generated from VR for smooth pursuit (SP) and the Visual Motion Sensitivity (VMS) tests are highly reliable for concussion detection. Furthermore, we train and evaluate these models, both qualitatively and quan-titatively. Our findings show these models can reach high true-positive-rates of around 99.9 percent of symptom provocation on the VR stimuli-based VOMS vs. current clinical manual VOMS.
{"title":"Virtual-Reality based Vestibular Ocular Motor Screening for Concussion Detection using Machine-Learning","authors":"Khondker Fariha Hossain, Sharif Amit Kamran, Prithul Sarker, Philip Pavilionis, I. Adhanom, N. Murray, A. Tavakkoli","doi":"10.48550/arXiv.2210.09295","DOIUrl":"https://doi.org/10.48550/arXiv.2210.09295","url":null,"abstract":". Sport-related concussion (SRC) depends on sensory information from visual, vestibular, and somatosensory systems. At the same time, the current clinical administration of Vestibular/Ocular Motor Screening (VOMS) is subjective and deviates among administrators. Therefore, for the assessment and manage-ment of concussion detection, standardization is required to lower the risk of injury and increase the validation among clinicians. With the advancement of technology, virtual reality (VR) can be utilized to advance the standardization of the VOMS, increasing the accuracy of testing administration and decreasing overall false positive rates. In this paper, we experimented with multiple machine learning methods to detect SRC on VR-generated data using VOMS. In our observation, the data generated from VR for smooth pursuit (SP) and the Visual Motion Sensitivity (VMS) tests are highly reliable for concussion detection. Furthermore, we train and evaluate these models, both qualitatively and quan-titatively. Our findings show these models can reach high true-positive-rates of around 99.9 percent of symptom provocation on the VR stimuli-based VOMS vs. current clinical manual VOMS.","PeriodicalId":91444,"journal":{"name":"Advances in visual computing : ... international symposium, ISVC ... : proceedings. International Symposium on Visual Computing","volume":"30 1","pages":"229-241"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83421033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-13DOI: 10.48550/arXiv.2210.06670
Khondker Fariha Hossain, A. Tavakkoli, S. Sengupta
In recent times deep learning has been widely used for automating various security tasks in Cyber Domains. However, adversaries manipulate data in many situations and diminish the deployed deep learning model's accuracy. One notable example is fooling CAPTCHA data to access the CAPTCHA-based Classifier leading to the critical system being vulnerable to cybersecurity attacks. To alleviate this, we propose a computational framework of game theory to analyze the CAPTCHA-based Classifier's vulnerability, strategy, and outcomes by forming a simultaneous two-player game. We apply the Fast Gradient Symbol Method (FGSM) and One Pixel Attack on CAPTCHA Data to imitate real-life scenarios of possible cyber-attack. Subsequently, to interpret this scenario from a Game theoretical perspective, we represent the interaction in the Stackelberg Game in Kuhn tree to study players' possible behaviors and actions by applying our Classifier's actual predicted values. Thus, we interpret potential attacks in deep learning applications while representing viable defense strategies in the game theory prospect.
{"title":"A Game Theoretical vulnerability analysis of Adversarial Attack","authors":"Khondker Fariha Hossain, A. Tavakkoli, S. Sengupta","doi":"10.48550/arXiv.2210.06670","DOIUrl":"https://doi.org/10.48550/arXiv.2210.06670","url":null,"abstract":"In recent times deep learning has been widely used for automating various security tasks in Cyber Domains. However, adversaries manipulate data in many situations and diminish the deployed deep learning model's accuracy. One notable example is fooling CAPTCHA data to access the CAPTCHA-based Classifier leading to the critical system being vulnerable to cybersecurity attacks. To alleviate this, we propose a computational framework of game theory to analyze the CAPTCHA-based Classifier's vulnerability, strategy, and outcomes by forming a simultaneous two-player game. We apply the Fast Gradient Symbol Method (FGSM) and One Pixel Attack on CAPTCHA Data to imitate real-life scenarios of possible cyber-attack. Subsequently, to interpret this scenario from a Game theoretical perspective, we represent the interaction in the Stackelberg Game in Kuhn tree to study players' possible behaviors and actions by applying our Classifier's actual predicted values. Thus, we interpret potential attacks in deep learning applications while representing viable defense strategies in the game theory prospect.","PeriodicalId":91444,"journal":{"name":"Advances in visual computing : ... international symposium, ISVC ... : proceedings. International Symposium on Visual Computing","volume":"3 1","pages":"369-380"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88910089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-12DOI: 10.48550/arXiv.2210.06474
Prithul Sarker, Nasif Zaman, A. Tavakkoli
The relative afferent asymmetry between two eyes can be diagnosed using swinging flashlight test, also known as the alternating light test. This remains one of the most used clinical tests to this day. Despite the swinging flashlight test's straightforward approach, a number of factors can add variability into the clinical methodology and reduce the measurement's validity and reliability. This includes small and poorly responsive pupils, dark iris, anisocoria, uneven illumination in both eyes. Due to these limitations, the true condition of relative afferent asymmetry may create confusion and various observers may quantify the relative afferent pupillary defect differently. Consequently, the results of the swinging flashlight test are subjective and ambiguous. In order to eliminate the limitations of traditional swinging flashlight test and introduce objectivity, we propose a novel approach to the swinging flashlight exam, VR-SFT, by making use of virtual reality (VR). We suggest that the clinical records of the subjects and the results of VR-SFT are comparable. In this paper, we describe how we exploit the features of immersive VR experience to create a reliable and objective swinging flashlight test.
{"title":"VR-SFT: Reproducing Swinging Flashlight Test in Virtual Reality to Detect Relative Afferent Pupillary Defect","authors":"Prithul Sarker, Nasif Zaman, A. Tavakkoli","doi":"10.48550/arXiv.2210.06474","DOIUrl":"https://doi.org/10.48550/arXiv.2210.06474","url":null,"abstract":"The relative afferent asymmetry between two eyes can be diagnosed using swinging flashlight test, also known as the alternating light test. This remains one of the most used clinical tests to this day. Despite the swinging flashlight test's straightforward approach, a number of factors can add variability into the clinical methodology and reduce the measurement's validity and reliability. This includes small and poorly responsive pupils, dark iris, anisocoria, uneven illumination in both eyes. Due to these limitations, the true condition of relative afferent asymmetry may create confusion and various observers may quantify the relative afferent pupillary defect differently. Consequently, the results of the swinging flashlight test are subjective and ambiguous. In order to eliminate the limitations of traditional swinging flashlight test and introduce objectivity, we propose a novel approach to the swinging flashlight exam, VR-SFT, by making use of virtual reality (VR). We suggest that the clinical records of the subjects and the results of VR-SFT are comparable. In this paper, we describe how we exploit the features of immersive VR experience to create a reliable and objective swinging flashlight test.","PeriodicalId":91444,"journal":{"name":"Advances in visual computing : ... international symposium, ISVC ... : proceedings. International Symposium on Visual Computing","volume":"20 1","pages":"193-204"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85268442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-16DOI: 10.48550/arXiv.2209.08200
Sejal Ghate, Alberto Santamaría-Pang, I. Tarapov, H. Sair, Craig K. Jones
Resting state fMRI is an imaging modality which reveals brain activity localization through signal changes, in what is known as Resting State Networks (RSNs). This technique is gaining popularity in neurosurgical pre-planning to visualize the functional regions and assess regional activity. Labeling of rs-fMRI networks require subject-matter expertise and is time consuming, creating a need for an automated classification algorithm. While the impact of AI in medical diagnosis has shown great progress; deploying and maintaining these in a clinical setting is an unmet need. We propose an end-to-end reproducible pipeline which incorporates image processing of rs-fMRI in a cloud-based workflow while using deep learning to automate the classification of RSNs. We have architected a reproducible Azure Machine Learning cloud-based medical imaging concept pipeline for fMRI analysis integrating the popular FMRIB Software Library (FSL) toolkit. To demonstrate a clinical application using a large dataset, we compare three neural network architectures for classification of deeper RSNs derived from processed rs-fMRI. The three algorithms are: an MLP, a 2D projection-based CNN, and a fully 3D CNN classification networks. Each of the net-works was trained on the rs-fMRI back-projected independent components giving>98% accuracy for each classification method.
{"title":"Deep Labeling of fMRI Brain Networks Using Cloud Based Processing","authors":"Sejal Ghate, Alberto Santamaría-Pang, I. Tarapov, H. Sair, Craig K. Jones","doi":"10.48550/arXiv.2209.08200","DOIUrl":"https://doi.org/10.48550/arXiv.2209.08200","url":null,"abstract":"Resting state fMRI is an imaging modality which reveals brain activity localization through signal changes, in what is known as Resting State Networks (RSNs). This technique is gaining popularity in neurosurgical pre-planning to visualize the functional regions and assess regional activity. Labeling of rs-fMRI networks require subject-matter expertise and is time consuming, creating a need for an automated classification algorithm. While the impact of AI in medical diagnosis has shown great progress; deploying and maintaining these in a clinical setting is an unmet need. We propose an end-to-end reproducible pipeline which incorporates image processing of rs-fMRI in a cloud-based workflow while using deep learning to automate the classification of RSNs. We have architected a reproducible Azure Machine Learning cloud-based medical imaging concept pipeline for fMRI analysis integrating the popular FMRIB Software Library (FSL) toolkit. To demonstrate a clinical application using a large dataset, we compare three neural network architectures for classification of deeper RSNs derived from processed rs-fMRI. The three algorithms are: an MLP, a 2D projection-based CNN, and a fully 3D CNN classification networks. Each of the net-works was trained on the rs-fMRI back-projected independent components giving>98% accuracy for each classification method.","PeriodicalId":91444,"journal":{"name":"Advances in visual computing : ... international symposium, ISVC ... : proceedings. International Symposium on Visual Computing","volume":"84 1","pages":"275-283"},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83846951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：