COVID-19 Infection Percentage Estimation from Computed Tomography Scans: Results and Insights from the International Per-COVID-19 Challenge.

COVID-19 analysis from medical imaging is an important task that has been intensively studied in the last years due to the spread of the COVID-19 pandemic. In fact, medical imaging has often been used as a complementary or main tool to recognize the infected persons. On the other hand, medical imaging has the ability to provide more details about COVID-19 infection, including its severity and spread, which makes it possible to evaluate the infection and follow-up the patient's state. CT scans are the most informative tool for COVID-19 infection, where the evaluation of COVID-19 infection is usually performed through infection segmentation. However, segmentation is a tedious task that requires much effort and time from expert radiologists. To deal with this limitation, an efficient framework for estimating COVID-19 infection as a regression task is proposed. The goal of the Per-COVID-19 challenge is to test the efficiency of modern deep learning methods on COVID-19 infection percentage estimation (CIPE) from CT scans. Participants had to develop an efficient deep learning approach that can learn from noisy data. In addition, participants had to cope with many challenges, including those related to COVID-19 infection complexity and crossdataset scenarios. This paper provides an overview of the COVID-19 infection percentage estimation challenge (Per-COVID-19) held at MIA-COVID-2022. Details of the competition data, challenges, and evaluation metrics are presented. The best performing approaches and their results are described and discussed.

A low-cost unity-based virtual training simulator for laparoscopic partial nephrectomy using HTC Vive.

Laparoscopic education and surgery assessments increase the success rates and lower the risks during actual surgeries. Hospital residents need a secure setting, and trainees require a safe and controlled environment with cost-effective resources where they may hone their laparoscopic abilities. Thus, we have modeled and developed a surgical simulator to provide the initial training in Laparoscopic Partial Nephrectomy (LPN-a procedure to treat kidney cancer or renal masses). To achieve this, we created a virtual simulator using an open-source game engine that can be used with a commercially available, reasonably priced virtual reality (VR) device providing visual and haptic feedback. In this study, the proposed simulator's design is presented, costs are contrasted, and the simulator's performance is assessed using face and content validity measures. CPU- and GPU-based computers can run the novel simulation with a soft body deformation based on simplex meshes. With a reasonable trade-off between price and performance, the HTC Vive's controlled soft body effect, physics-based deformation, and haptic rendering offer the advantages of an excellent surgical simulator. The trials show that the medical volunteers who performed the initial LPN procedures for newbie surgeons received positive feedback.