The Unusual Presence of Gas From a Puncture Wound: A Case Report.

The presence of gas within soft tissues as suggested by plain film radiographs and magnetic resonance imaging is usually sufficient evidence for a gas-producing bacterial infection. A thorough clinical examination and history and tissue culture are necessary to better determine the source of the gas. However, despite the unremarkable physical examination findings, the present case of a plantar puncture wound rapidly developed gas in the tissues and warranted surgical exploration and repair. Delaying treatment in any case of potential gas gangrene can be limb- and life-threatening. Only later was it revealed by the patient's husband that the wound might have been contaminated soon after the injury from a source other than the puncture, which led to the early presentation of gas on the imaging studies.

DLSIA: Deep Learning for Scientific Image Analysis.

DLSIA (Deep Learning for Scientific Image Analysis) is a Python-based machine learning library that empowers scientists and researchers across diverse scientific domains with a range of customizable convolutional neural network (CNN) architectures for a wide variety of tasks in image analysis to be used in downstream data processing. DLSIA features easy-to-use architectures, such as autoencoders, tunable U-Nets and parameter-lean mixed-scale dense networks (MSDNets). Additionally, this article introduces sparse mixed-scale networks (SMSNets), generated using random graphs, sparse connections and dilated convolutions connecting different length scales. For verification, several DLSIA-instantiated networks and training scripts are employed in multiple applications, including inpainting for X-ray scattering data using U-Nets and MSDNets, segmenting 3D fibers in X-ray tomographic reconstructions of concrete using an ensemble of SMSNets, and leveraging autoencoder latent spaces for data compression and clustering. As experimental data continue to grow in scale and complexity, DLSIA provides accessible CNN construction and abstracts CNN complexities, allowing scientists to tailor their machine learning approaches, accelerate discoveries, foster interdisciplinary collaboration and advance research in scientific image analysis.

Chondromyxoid fibroma of the calcaneus: two case reports and literature review.

Chondromyxoid fibroma occurs primarily in the long tubular bones of the lower extremity, with the foot representing the second most frequent location after the knee. This benign cartilaginous tumor of bone is currently the rarest reported neoplasm of cartilaginous origin. This mass can mimic other benign and malignant bone tumors owing to its variable histologic features. We report 2 cases of chondromyxoid fibroma of the calcaneus with varying presentations. Initially, advanced imaging studies pointed to a diagnosis of a unicameral bone cyst. Pathologic examination is difficult but can be used to differentiate this lesion from more serious conditions. A quick and accurate diagnosis of chondromyxoid fibroma can prevent unnecessary treatment that could be harmful to the patient.

Deploying Machine Learning Based Segmentation for Scientific Imaging Analysis at Synchrotron Facilities.

Scientific user facilities present a unique set of challenges for image processing due to the large volume of data generated from experiments and simulations. Furthermore, developing and implementing algorithms for real-time processing and analysis while correcting for any artifacts or distortions in images remains a complex task, given the computational requirements of the processing algorithms. In a collaborative effort across multiple Department of Energy national laboratories, the "MLExchange" project is focused on addressing these challenges. MLExchange is a Machine Learning framework deploying interactive web interfaces to enhance and accelerate data analysis. The platform allows users to easily upload, visualize, label, and train networks. The resulting models can be deployed on real data while both results and models could be shared with the scientists. The MLExchange web-based application for image segmentation allows for training, testing, and evaluating multiple machine learning models on hand-labeled tomography data. This environment provides users with an intuitive interface for segmenting images using a variety of machine learning algorithms and deep-learning neural networks. Additionally, these tools have the potential to overcome limitations in traditional image segmentation techniques, particularly for complex and low-contrast images.

Pedal occurrence of nodular fasciitis: a case report.

Nodular fasciitis occurs primarily in the soft tissue structures of the upper extremities and, more rarely, in the lower extremities. This mass, although benign, can mimic certain sarcomas and is therefore important to differentiate from more serious conditions. We report a case of nodular fasciitis of the foot in a healthy 47-year-old male who presented with increasing pain and swelling in his right third digit of 3 months duration. Initial radiographs revealed an irregular contour to the proximal phalanx with increased soft tissue density. Magnetic resonance imaging and computed tomography scans were obtained that revealed a soft tissue mass with bone erosion and fracture. Histologic analysis from a specimen obtained after excision of the lesion confirmed the diagnosis of hyalinizing nodular fasciitis. Nodular fasciitis in the foot can appear malignant from the clinical and histopathologic findings but can be differentiated. A quick and accurate diagnosis of this benign process can prevent a treatment program unnecessarily dangerous to the patient.

A comparison of deep-learning-based inpainting techniques for experimental X-ray scattering.

The implementation is proposed of image inpainting techniques for the reconstruction of gaps in experimental X-ray scattering data. The proposed methods use deep learning neural network architectures, such as convolutional autoencoders, tunable U-Nets, partial convolution neural networks and mixed-scale dense networks, to reconstruct the missing information in experimental scattering images. In particular, the recovered pixel intensities are evaluated against their corresponding ground-truth values using the mean absolute error and the correlation coefficient metrics. The results demonstrate that the proposed methods achieve better performance than traditional inpainting algorithms such as biharmonic functions. Overall, tunable U-Net and mixed-scale dense network architectures achieved the best reconstruction performance among all the tested algorithms, with correlation coefficient scores greater than 0.9980.

MLExchange: A web-based platform enabling exchangeable machine learning workflows for scientific studies.

Machine learning (ML) algorithms are showing a growing trend in helping the scientific communities across different disciplines and institutions to address large and diverse data problems. However, many available ML tools are programmatically demanding and computationally costly. The MLExchange project aims to build a collaborative platform equipped with enabling tools that allow scientists and facility users who do not have a profound ML background to use ML and computational resources in scientific discovery. At the high level, we are targeting a full user experience where managing and exchanging ML algorithms, workflows, and data are readily available through web applications. Since each component is an independent container, the whole platform or its individual service(s) can be easily deployed at servers of different scales, ranging from a personal device (laptop, smart phone, etc.) to high performance clusters (HPC) accessed (simultaneously) by many users. Thus, MLExchange renders flexible using scenarios-users could either access the services and resources from a remote server or run the whole platform or its individual service(s) within their local network.