Hidden in Muscles: A Case Study of Intramuscular Lipoma.

This case report presents a 64-year-old male with a giant intramuscular lipoma on the right lumbar region's latissimus dorsi muscle. The patient presented with painless swelling, which gradually increased over six years. Magnetic resonance imaging (MRI) confirmed the presence of the lipoma, prompting surgical intervention. The surgical procedure involved meticulous dissection and complete excision of the tumor. Histopathological examination validated the diagnosis. Comparative analyses with similar cases highlighted variations in surgical approaches and the challenges in managing intramuscular lipomas. This case emphasizes the importance of considering intramuscular lipomas in soft tissue mass differentials and the significance of comprehensive management strategies for optimal patient outcomes.

Atypical Giant Lipoma in the Left Lower Abdomen: A Case Study.

In the present case report, we discuss a case of an uncommon giant lipoma in the left lower abdomen of a 55-year-old female. The case is presented to highlight the need to consider lipoma as one of the possibilities in cases with subcutaneous masses. Despite its abnormal location and considerable magnitude, surgical excision under short general anesthesia resulted in successful outcomes without immediate complications. Finally, by comparing with similar circumstances, it is possible to recognize that individualized management strategies based on patient characteristics can benefit surgical considerations. The significant message is that timely intervention, comprehensive assessment, and teamwork are essential in achieving satisfactory outcomes among patients with rare cases of lipomas, such as this one.

Microsaccade-inspired event camera for robotics.

Neuromorphic vision sensors or event cameras have made the visual perception of extremely low reaction time possible, opening new avenues for high-dynamic robotics applications. These event cameras' output is dependent on both motion and texture. However, the event camera fails to capture object edges that are parallel to the camera motion. This is a problem intrinsic to the sensor and therefore challenging to solve algorithmically. Human vision deals with perceptual fading using the active mechanism of small involuntary eye movements, the most prominent ones called microsaccades. By moving the eyes constantly and slightly during fixation, microsaccades can substantially maintain texture stability and persistence. Inspired by microsaccades, we designed an event-based perception system capable of simultaneously maintaining low reaction time and stable texture. In this design, a rotating wedge prism was mounted in front of the aperture of an event camera to redirect light and trigger events. The geometrical optics of the rotating wedge prism allows for algorithmic compensation of the additional rotational motion, resulting in a stable texture appearance and high informational output independent of external motion. The hardware device and software solution are integrated into a system, which we call artificial microsaccade-enhanced event camera (AMI-EV). Benchmark comparisons validated the superior data quality of AMI-EV recordings in scenarios where both standard cameras and event cameras fail to deliver. Various real-world experiments demonstrated the potential of the system to facilitate robotics perception both for low-level and high-level vision tasks.

Ajna: Generalized deep uncertainty for minimal perception on parsimonious robots.

Robots are active agents that operate in dynamic scenarios with noisy sensors. Predictions based on these noisy sensor measurements often lead to errors and can be unreliable. To this end, roboticists have used fusion methods using multiple observations. Lately, neural networks have dominated the accuracy charts for perception-driven predictions for robotic decision-making and often lack uncertainty metrics associated with the predictions. Here, we present a mathematical formulation to obtain the heteroscedastic aleatoric uncertainty of any arbitrary distribution without prior knowledge about the data. The approach has no prior assumptions about the prediction labels and is agnostic to network architecture. Furthermore, our class of networks, Ajna, adds minimal computation and requires only a small change to the loss function while training neural networks to obtain uncertainty of predictions, enabling real-time operation even on resource-constrained robots. In addition, we study the informational cues present in the uncertainties of predicted values and their utility in the unification of common robotics problems. In particular, we present an approach to dodge dynamic obstacles, navigate through a cluttered scene, fly through unknown gaps, and segment an object pile, without computing depth but rather using the uncertainties of optical flow obtained from a monocular camera with onboard sensing and computation. We successfully evaluate and demonstrate the proposed Ajna network on four aforementioned common robotics and computer vision tasks and show comparable results to methods directly using depth. Our work demonstrates a generalized deep uncertainty method and demonstrates its utilization in robotics applications.

Outlandish pancreatic pseudocyst: A case report.

Pseudocysts in the perinephric region are rare and pose a diagnostic dilemma. We present the case of a 54-year-old male with left perirenal pancreatic pseudocyst. The diagnosis was enabled via proper clinical history taking and imaging investigations. The patient was successfully managed with definitive primary surgical treatment. This report highlights difficulties in diagnosis and treatment.