Geforce gtx 980 ti

The results generated by our models were compared with the results from cvi42 (Circle Cardiovascular Imaging Inc) on our test set that included CMR images from $38$ patients. All volumetric measures were calculated using OsiriX Lite software (Pixmeo, Bernex, Switzerland). To calculate the volume, small modifications were applied to the open source plugin available at https://github.com/chrischute/numpy2roi to make the format consistent with our dataset. The segmented CMR images were converted into OsiriX’s .roi files using the modified plugin. The resulted .roi files were imported to the OsiriX Lite software for volume calculation through its built-in 3D construction algorithm.
Our method was developed using the Python 2.7.12 and performed on a workstation with Intel(R) Core (TM) i7 − 5930 K CPU 3.50 GHz with four NVIDIA GeForce GTX 980 Ti GPUs, on a 64 − bit Ubuntu platform.

Eye movements were recorded with a binocular, head-mounted Pupil Core eye-tracker (Pupil Labs UG {haftungsbeschränkt}, Berlin, Germany). The eye cameras recorded at 120 Hz at 640×480 pixel resolution while the forward-facing scene camera recorded at 60 Hz at 1,280×720 resolution. The open-source Pupil Capture software (https://github.com/pupil-labs/pupil) was used for recording and calibration. Four optical markers were placed on screen corners to allow for mapping gaze from the headset's forward-facing scene camera image to screen coordinates.
The experiment was presented on a LG OLED55C7V 55” screen with participants sitting at a fixed distance of 85 cm on a Playseat Evolution gaming chair (Playseat Evolution Alcantara, Playseats B.V., the Netherlands). The software for the tracking task was developed in-house and is available as open-source (https://github.com/jampekka/webtrajsim/tree/speedest18). All software ran on an HP ENVY Phoenix 860-081no (Intel Core i7-6700K CPU, NVIDIA GeForce GTX 980 TI GPU) desktop computer running a Debian GNU/Linux as the operating system.