Endometriosis is a non-malignant disorder that affects 176 million women globally. Diagnostic delays result in severe dysmenorrhea, dyspareunia, chronic pelvic pain, and infertility. Therefore, there is a significant need to diagnose patients at an early stage. Our objective in this work is to investigate the potential of deep learning methods to classify endometriosis from ultrasound data. Retrospective data from 100 subjects were collected at the Rutgers Robert Wood Johnson University Hospital (New Brunswick, NJ, USA). Endometriosis was diagnosed via laparoscopy or laparotomy. We designed and trained five different deep learning methods (Xception, Inception-V4, ResNet50, DenseNet, and EfficientNetB2) for the classification of endometriosis from ultrasound data. Using 5-fold cross-validation study we achieved an average area under the receiver operator curve (AUC) of 0.85 and 0.90 respectively for the two evaluation studies.
Significance: Interaction of neurons with their extracellular environment and the mechanical forces at focal adhesions and synaptic junctions play important roles in neuronal development.
Aim: To advance studies of mechanotransduction, we demonstrate the use of the vinculin tension sensor (VinTS) in primary cultures of cortical neurons. VinTS consists of TS module (TSMod), a Förster resonance energy transfer (FRET)-based tension sensor, inserted between vinculin’s head and tail. FRET efficiency decreases with increased tension across vinculin.
Approach: Primary cortical neurons cultured on glass coverslips coated with poly-d-lysine and laminin were transfected with plasmids encoding untargeted TSMod, VinTS, or tail-less vinculinTS (VinTL) lacking the actin-binding domain. The neurons were imaged between day in vitro (DIV) 5 to 8. We detail the image processing steps for calculation of FRET efficiency and use this system to investigate the expression and FRET efficiency of VinTS in growth cones.
Results: The distribution of fluorescent constructs was similar within growth cones at DIV 5 to 8. The mean FRET efficiency of TSMod (28.5 ± 3.6 % ) in growth cones was higher than the mean FRET efficiency of VinTS (24.6 ± 2 % ) and VinTL (25.8 ± 1.8 % ) (p < 10 − 6). While small, the difference between the FRET efficiency of VinTS and VinTL was statistically significant (p < 10 − 3), suggesting that vinculin is under low tension in growth cones. Two-hour treatment with the Rho-associated kinase inhibitor Y-27632 did not affect the mean FRET efficiency. Growth cones exhibited dynamic changes in morphology as observed by time-lapse imaging. VinTS FRET efficiency showed greater variance than TSMod FRET efficiency as a function of time, suggesting a greater dependence of VinTS FRET efficiency on growth cone dynamics compared with TSMod.
Conclusions: The results demonstrate the feasibility of using VinTS to probe the function of vinculin in neuronal growth cones and provide a foundation for studies of mechanotransduction in neurons using this tension probe.
A 3D kinematic measurement of joint movement is crucial for orthopedic surgery assessment and diagnosis. This is usually obtained through a frame-by-frame registration of the 3D bone volume to a fluoroscopy video of the joint movement. The high cost of a high-quality fluoroscopy imaging system has hindered the access of many labs to this application. This is while the more affordable and low-dosage version, the mini C-arm, is not commonly used for this application due to low image quality. In this paper, we introduce a novel method for kinematic analysis of joint movement using the mini C-arm. In this method the bone of interest is recovered and isolated from the rest of the image using a non-rigid registration of an atlas to each frame. The 3D/2D registration is then performed using the weighted histogram of image gradients as an image feature. In our experiments, the registration error was 0.89 mm and 2.36° for human C2 vertebra. While the precision is still lacking behind a high quality fluoroscopy machine, it is a good starting point facilitating the use of mini C-arms for motion analysis making this application available to lower-budget environments. Moreover, the registration was highly resistant to the initial distance from the true registration, converging to the answer from anywhere within ±90° of it.
The segmentation of bone surfaces from magnetic resonance imaging (MRI) data has applications in the quantitative measurement of knee osteoarthritis, surgery planning for patient-specific total knee arthroplasty, and its subsequent fabrication of artificial implants. However, due to the problems associated with MRI imaging, such as low contrast between bone and surrounding tissues, noise, bias fields, and the partial volume effect, segmentation of bone surfaces continues to be a challenging operation. A framework is presented for the enhancement of knee MRI scans prior to segmentation in order to obtain high contrast bone images. During the first stage, a contrast enhanced relative total variation regularization method is used in order to remove textural noise from the bone structures and surrounding soft tissue interface. This salient bone edge information is further enhanced using a sparse gradient counting method based on L0 gradient minimization, which globally controls how many nonzero gradients are resulted in order to approximate prominent bone structures in a structure-sparsity-management manner. The last stage of the framework involves incorporation of local phase bone boundary information in order to provide an intensity invariant enhancement of contrast between the bone and surrounding soft tissue. The enhanced images are segmented using a fast random walker algorithm. Validation against expert segmentation was performed on 20 clinical knee MRI volumes and achieved a mean dice similarity coefficient of 0.949.
The segmentation of bone surfaces from magnetic resonance imaging (MRI) data has applications in the quanti- tative measurement of knee osteoarthritis, surgery planning for patient specific total knee arthroplasty and its subsequent fabrication of artificial implants. However, due to the problems associated with MRI imaging such as low contrast between bone and surrounding tissues, noise, bias fields, and the partial volume effect, segmentation of bone surfaces continues to be a challenging operation. In this paper, a new framework is presented for the enhancement of knee MRI scans prior to segmentation in order to obtain high contrast bone images. During the first stage, a new contrast enhanced relative total variation (RTV) regularization method is used in order to remove textural noise from the bone structures and surrounding soft tissue interface. This salient bone edge information is further enhanced using a sparse gradient counting method based on L0 gradient minimization, which globally controls how many non-zero gradients are resulted in order to approximate prominent bone structures in a structure-sparsity-management manner. The last stage of the framework involves incorporation of local phase bone boundary information in order to provide an intensity invariant enhancement of contrast between the bone and surrounding soft tissue. The enhanced images are segmented using a fast random walker algorithm. Validation against expert segmentation was performed on 10 clinical knee MRI images, and achieved a mean dice similarity coefficient (DSC) of 0.975.
KEYWORDS: 3D modeling, Image registration, Bone, 3D image processing, Distance measurement, 3D image reconstruction, Computed tomography, Medical imaging, Image restoration, Video
Three dimensional (3D) to two dimensional (2D) image registration is crucial in many medical applications such as image-guided evaluation of musculoskeletal disorders. One of the key problems is to estimate the 3D CT- reconstructed bone model positions (translation and rotation) which maximize the similarity between the digitally reconstructed radiographs (DRRs) and the 2D fluoroscopic images using a registration method. This problem is computational-intensive due to a large search space and the complicated DRR generation process. Also, finding a similarity measure which converges to the global optimum instead of local optima adds to the challenge. To circumvent these issues, most existing registration methods need a manual initialization, which requires user interaction and is prone to human error. In this paper, we introduce a novel feature-based registration method using the weighted histogram of gradient directions of images. This method simplifies the computation by searching the parameter space (rotation and translation) sequentially rather than simultaneously. In our numeric simulation experiments, the proposed registration algorithm was able to achieve sub-millimeter and sub-degree accuracies. Moreover, our method is robust to the initial guess. It can tolerate up to ±90°rotation offset from the global optimal solution, which minimizes the need for human interaction to initialize the algorithm.
Percutaneous treatment of scaphoid fractures has found increasing interest in recent years as it promises to minimize soft-tissue damage, and minimizes the risk of infections and the loss of the joint stability. However, as this procedure is mostly performed on 2D fluoroscopic images, the accurate localization of the scaphoid bone for fracture fixation renders extremely challenging. In this work, we thus propose the integration of a statistical wrist model with 3D intraoperative ultrasound for accurate localization of the scaphoid bone. We utilize a previously developed statistical wrist model and register it to bone surfaces in ultrasound images using a probabilistic approach that involves expectation-maximization. We utilize local phase symmetry to detect features in noisy ultrasound images; in addition, we use shadow information in ultrasound images to enhance and set apart bone from other features. Feasibility experiments are performed by registering the wrist model to 3D ultrasound volumes of two different wrists at two different wrist positions. And the result indicates a potential of the proposed technique for localization of the scaphoid bone in ultrasound images.
During percutaneous lumbar spine needle interventions, alignment of the preoperative computed tomography (CT) with intraoperative ultrasound (US) can augment anatomical visualization for the clinician. We propose an approach to rigidly align CT and US data of the lumbar spine. The approach involves an intensity-based volume registration step, followed by a surface segmentation and a point-based registration of the entire lumbar spine volume. A clinical feasibility study resulted in mean registration error of approximately 3 mm between CT and US data.
Stitching of volumes obtained from three dimensional (3D) ultrasound (US) scanners improves visualization of anatomy
in many clinical applications. Fast but accurate volume registration remains the key challenge in this area.We propose a
volume stitching method based on efficient registration of 3D US volumes obtained from a tracked US probe. Since the
volumes, after adjusting for probe motion, are coarsely registered, we obtain salient correspondence points in the central
slices of these volumes. This is done by first removing artifacts in the US slices using intensity invariant local phase
image processing and then applying the Harris Corner detection algorithm. Fast sub-volume registration on a small
neighborhood around the points then gives fast, accurate 3D registration parameters. The method has been tested on 3D
US scans of phantom and real human radius and pelvis bones and a phantom human fetus. The method has also been
compared to volumetric registration, as well as feature based registration using 3D-SIFT. Quantitative results show
average post-registration error of 0.33mm which is comparable to volumetric registration accuracy (0.31mm) and much
better than 3D-SIFT based registration which failed to register the volumes. The proposed method was also much faster
than volumetric registration (~4.5 seconds versus 83 seconds).
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