Three-dimensional Segmentation of the Scoliotic Spine from MRI using Unsupervised Volume-based MR-CT Synthesis

Vertebral bone segmentation from magnetic resonance (MR) images is a challenging task. Due to the inherent nature of the modality to emphasize soft tissues of the body, common thresholding algorithms are ineffective in detecting bones in MR images. On the other hand, it is relatively easier to segment bones from CT images because of the high contrast between bones and the surrounding regions. For this reason, we perform a cross-modality synthesis between MR and CT domains for simple thresholding-based segmentation of the vertebral bones. However, this implicitly assumes the availability of paired MR-CT data, which is rare, especially in the case of scoliotic patients. In this paper, we present a completely unsupervised, fully three-dimensional (3D) cross-modality synthesis method for segmenting scoliotic spines. A 3D CycleGAN model is trained for an unpaired volume-to-volume translation across MR and CT domains. Then, the Otsu thresholding algorithm is applied to the synthesized CT volumes for easy segmentation of the vertebral bones. The resulting segmentation is used to reconstruct a 3D model of the spine. We validate our method on 28 scoliotic vertebrae in 3 patients by computing the point-to-surface mean distance between the landmark points for each vertebra obtained from pre-operative X-rays and the surface of the segmented vertebra. Our study results in a mean error of 3.41 $\pm$ 1.06 mm. Based on qualitative and quantitative results, we conclude that our method is able to obtain a good segmentation and 3D reconstruction of scoliotic spines, all after training from unpaired data in an unsupervised manner.Comment: To appear in the Proceedings of the SPIE Medical Imaging Conference 2021, San Diego, CA. 9 pages, 4 figures in tota

Machine Learning towards General Medical Image Segmentation

The quality of patient care associated with diagnostic radiology is proportionate to a physician\u27s workload. Segmentation is a fundamental limiting precursor to diagnostic and therapeutic procedures. Advances in machine learning aims to increase diagnostic efficiency to replace single applications with generalized algorithms. We approached segmentation as a multitask shape regression problem, simultaneously predicting coordinates on an object\u27s contour while jointly capturing global shape information. Shape regression models inherent point correlations to recover ambiguous boundaries not supported by clear edges and region homogeneity. Its capabilities was investigated using multi-output support vector regression (MSVR) on head and neck (HaN) CT images. Subsequently, we incorporated multiplane and multimodality spinal images and presented the first deep learning multiapplication framework for shape regression, the holistic multitask regression network (HMR-Net). MSVR and HMR-Net\u27s performance were comparable or superior to state-of-the-art algorithms. Multiapplication frameworks bridges any technical knowledge gaps and increases workflow efficiency

3D Ultrasound in the Management of Post Hemorrhagic Ventricle Dilatation

Enlargement of the cerebral ventricles is relatively common among extremely preterm neonates born before 28 weeks gestational age. One common cause of ventricle dilatation is post hemorrhagic ventricle dilatation following a bleed in the cerebral ventricles. While many neonates with PHVD will have spontaneous resolution of the condition, severe, persistent PHVD is associated with a greater risk of brain injury and morbidity later in life and left untreated can cause death. The current clinical management strategy consists of daily measurements of head circumference and qualitative interpretation of two-dimensional US images to detect ventricular enlargement and monitoring vital signs for indications increased intracranial pressure (i.e. apnea, bradycardia). Despite the widespread clinical use of these indicators, they do not have the specificity to reliably indicate when an intervention to remove some CSF is required to prevent brain damage. Early recognition of interventional necessity using quantitative measurements could help with the management of the disease, and could lead to better care in the future. Our objective was to develop and validate a three-dimensional ultrasound system for use within an incubator of neonates with PHVD in order to accurately measure the cerebral ventricle volume. This system was validated against known geometric phantoms as well as a custom ventricle-like phantom. Once validated, the system was used in a clinical study of 70 neonates with PHVD to measure the ventricle size. In addition to three-dimensional ultrasound, clinical ultrasound images, and MRIs were attained. Clinical measurements of the ventricles and three-dimensional ultrasound ventricle volumes were used to determine thresholds between neonates with PHVD who did and did not receive interventions based on current clinical management. We determined image based thresholds for intervention for both neonates who will receive an initial intervention, as well as those who will receive multiple interventions. Three-dimensional ultrasound based ventricle volume measurements had high sensitivity and specificity as patients with persistent PHVD have ventricles that increase in size faster than those who undergo resolution. This allowed for delineation between interventional and non-interventional patients within the first week of life. While this is still a small sample size study, these results can give rise to larger studies that would be able to determine if earlier intervention can result in better neurodevelopmental outcomes later in life

Correlation time diffusion coefficient age related dependency: from 6 months to 24 years old

Diffusion MRI is established as an essential tool for both clinicians as well as biomedical scientists. Its application plays an important role in diagnosis and management of acute stroke, tumors, trauma, and infectious disease, among myriad other applications. Furthermore, diffusion studies are crucial for understanding disease processes caused by developmental and neurodegenerative disorders. The latest developments in quantitative diffusion imaging have broadened the potential application of the technique for both clinical and research applications. However, ongoing research is critical in order to further improve the accuracy and reproducibility of quantitative diffusion MRI techniques. Correlation time diffusion (D-CT) is emerging as an alternative technique for obtaining diffusion qMRI data[1][2][3]. Using the D-CT technique, T1 relaxation data is analyzed, using a modified BPP relaxation theory, in order to calculate the correlation times of protons’ stochastic processes and relate these times to solution viscosity in order to calculate proton diffusion coefficients, ADCs. The purpose of our study was to compare age related changes, during childhood and early adulthood, of global brain diffusion coefficients obtained by correlation time technique to global brain diffusion coefficients obtained by a conventional pulsed field gradient technique. In our study, we used the data of 27 subjects (0.5-24 years old), who were scanned with Mixed-TSE and DW-SS-SE-EPI pulse sequences. Subsequently, we processed the resulting directly acquired images to generate T1, T2, PD, ADC maps as well as volumetric data. We used the student t-test and linear regression analysis to compare and interpret our data. Our results show a strong positive correlation between the volumetric data. Good correlation between ADC values was observed, with the widest discrepancy between DCT, DPFG (about 17%) observed in the youngest subjects, and the smallest discrepancy noted in the older subjects

A Two-Tier Framework Based on GoogLeNet and YOLOv3 Models for Tumor Detection in MRI

Medical Image Analysis (MIA) is one of the active research areas in computer vision, where brain tumor detection is the most investigated domain among researchers due to its deadly nature. Brain tumor detection in magnetic resonance imaging (MRI) assists radiologists for better analysis about the exact size and location of the tumor. However, the existing systems may not efficiently classify the human brain tumors with significantly higher accuracies. In addition, smart and easily implementable approaches are unavailable in 2D and 3D medical images, which is the main problem in detecting the tumor. In this paper, we investigate various deep learning models for the detection and localization of the tumor in MRI. A novel two-tier framework is proposed where the first tire classifies normal and tumor MRI followed by tumor regions localization in the second tire. Furthermore, in this paper, we introduce a well-annotated dataset comprised of tumor and normal images. The experimental results demonstrate the effectiveness of the proposed framework by achieving 97% accuracy using GoogLeNet on the proposed dataset for classification and 83% for localization tasks after fine-tuning the pre-trained you only look once (YOLO) v3 model

Action Sport Cameras As An Instrument To Perform A 3d Underwater Motion Analysis

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Action sport cameras (ASC) are currently adopted mainly for entertainment purposes but their uninterrupted technical improvements, in correspondence of cost decreases, are going to disclose them for three-dimensional (3D) motion analysis in sport gesture study and athletic performance evaluation quantitatively. Extending this technology to sport analysis however still requires a methodologic step-forward to making ASC a metric system, encompassing ad-hoc camera setup, image processing, feature tracking, calibration and 3D reconstruction. Despite traditional laboratory analysis, such requirements become an issue when coping with both indoor and outdoor motion acquisitions of athletes. In swimming analysis for example, the camera setup and the calibration protocol are particularly demanding since land and underwater cameras are mandatory. In particular, the underwater camera calibration can be an issue affecting the reconstruction accuracy. In this paper, the aim is to evaluate the feasibility of ASC for 3D underwater analysis by focusing on camera setup and data acquisition protocols. Two GoPro Hero3+ Black (frequency: 60Hz; image resolutions: 1280x720/1920x1080 pixels) were located underwater into a swimming pool, surveying a working volume of about 6m(3). A two-step custom calibration procedure, consisting in the acquisition of one static triad and one moving wand, carrying nine and one spherical passive markers, respectively, was implemented. After assessing camera parameters, a rigid bar, carrying two markers at known distance, was acquired in several positions within the working volume. The average error upon the reconstructed inter-marker distances was less than 2.5mm (1280x720) and 1.5mm (1920x1080). The results of this study demonstrate that the calibration of underwater ASC is feasible enabling quantitative kinematic measurements with accuracy comparable to traditional motion capture systems.118Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (Sao Paulo Research Foundation) [00/1293-1, 2006/02403-1, 2009/09359-6]Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (National Counsel of Technological and Scientific Development) [473729/2008-3, 304975/2009-5, 478120/2011-7, 234088/2014-1, 481391/2013-4]Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (Brazilian Federal Agency for Support and Evaluation of Graduation Education) [2011/10-7, 08/2014]Fundacao de Amparo a Pesquisa de Minas Gerais (Minas Gerais Research Foundation) [PEE-00596-14]Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES