2020
DOI: 10.1007/978-3-030-41309-5_8
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Using Augmented Reality and Machine Learning in Radiology

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Cited by 8 publications
(8 citation statements)
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“…Surgeries are one of the main cost factors of healthcare systems. To reduce the costs related to diagnoses and surgeries, we have previously proposed a system for automated segmentation of medical images in order to segment body parts like liver or lesions (Trestioreanu et al 2020 ). The model is based on convolutional neural networks, for which we showed promising results on real computed tomography scans.…”
Section: Applications In Healthcarementioning
confidence: 99%
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“…Surgeries are one of the main cost factors of healthcare systems. To reduce the costs related to diagnoses and surgeries, we have previously proposed a system for automated segmentation of medical images in order to segment body parts like liver or lesions (Trestioreanu et al 2020 ). The model is based on convolutional neural networks, for which we showed promising results on real computed tomography scans.…”
Section: Applications In Healthcarementioning
confidence: 99%
“…
Fig. 4 3D visualization of liver segmentation in a Microsoft HoloLens (left): the segmented liver volume (right) was separated from the input volume—a scanned torso (centre) (Trestioreanu et al 2020 ). Source: author
…”
Section: Applications In Healthcarementioning
confidence: 99%
“…With the basic feature extraction, they were able to create a system that was easier to use than that of traditional inputs. However, the system was not utilising XR, gesture recognition was done through a "Leap Motion device" and users had the 3D volume visualised still on a 2D screen, giving a disconnect between V. Ferrari et al [13] et al [14] F. Cutolo et al [17] X. Wu et al [16] F. Cutolo et al [17] TABLE I: Key works in extended reality applied to medical imaging the user and their spatial understanding of the volume. There are limited examples of systems utilising XR.…”
Section: Extended Reality In Medical Image Analysismentioning
confidence: 99%
“…We chose SLIC Superpixels segmentation [34] for the realtime system due to its efficiency to provide fast segmentation results back to the user. However, early iterations of alternative segmentation methods deployed in XR [14] have been emerging. With a proof of concept, [14] integrated a machine learning approaches for XR.…”
Section: Segmentationmentioning
confidence: 99%
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