ArticleInternational journal of computer assisted radiology and surgery2021
Real-time deep learning semantic segmentation during intra-operative surgery for 3D augmented reality assistance.
Article in International journal of computer assisted radiology and surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Augmented reality in robotic urology: from 3D printed models to cognitive surgical systems. A systematic literature review.Journal of robotic surgery · 2026Pooled it
- Augmented Reality (AR) for Surgical Robotic and Autonomous Systems: State of the Art, Challenges, and Solutions.Sensors (Basel, Switzerland) · 2023Pooled it
- Artificial intelligence in robotic urologic surgery: a scoping review.World journal of urology · 2026Article
- Artificial intelligence and surgical data science in robot-assisted radical prostatectomy: global research trends, knowledge structure, and emerging frontiers.Journal of robotic surgery · 2026Review
- Artificial intelligence-driven real-time assistance in minimally invasive surgery: a technology-oriented narrative review.Journal of minimally invasive surgery · 2026Review
- Artificial intelligence in urology training Enhancing annotation, feedback, and evaluation in robotic, laparoscopic, and endoscopic surgery.Canadian Urological Association journal = Journal de l'Association des urologues du Canada · 2026Review
- A hybrid self-supervised teacher-student model for predicting neurovascular bundle preservation in prostatectomy videos.International journal of computer assisted radiology and surgery · 2026Article
- Artificial Intelligence in Adult Cardiovascular Medicine and Surgery: Real-World Deployments and Outcomes.Journal of personalized medicine · 2026Review
- Bibliometric analysis of deep learning for surgical instrument segmentation, detection and tracking in minimally invasive surgery.Frontiers in digital health · 2026Review
- Automated multimodel segmentation and tracking for AR-guided open liver surgery using scene-aware self-prompting.International journal of computer assisted radiology and surgery · 2025Article
- Current Applications and Limitations of Augmented Reality in Urological Surgery: A Practical Primer and 'State of the Field'.Current urology reports · 2025Review
- An augmented reality overlay for navigated prostatectomy using fiducial-free 2D-3D registration.International journal of computer assisted radiology and surgery · 2025Article
- Anatomical recognition of dissection layers, nerves, vas deferens, and microvessels using artificial intelligence during transabdominal preperitoneal inguinal hernia repair.Hernia : the journal of hernias and abdominal wall surgery · 2024Article
- Automatic 3D Augmented-Reality Robot-Assisted Partial Nephrectomy Using Machine Learning: Our Pioneer Experience.Cancers · 2024Article
- A Self-Configuring Deep Learning Network for Segmentation of Temporal Bone Anatomy in Cone-Beam CT Imaging.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2023Article
- A Systematic Review of New Imaging Technologies for Robotic Prostatectomy: From Molecular Imaging to Augmented Reality.Journal of clinical medicine · 2023Review
- A scoping review of deep learning in cancer nursing combined with augmented reality: The era of intelligent nursing is coming.Asia-Pacific journal of oncology nursing · 2022Article
- A deep learning framework for real-time 3D model registration in robot-assisted laparoscopic surgery.The international journal of medical robotics + computer assisted surgery : MRCAS · 2022Article
- Computer Vision and Machine-Learning Techniques for Automatic 3D Virtual Images Overlapping During Augmented Reality Guided Robotic Partial Nephrectomy.Technology in cancer research & treatmentArticle
- Laparoscopic image segmentation of the hepatobiliary system: Current status and advances from a deep learning perspective.Digital healthReview
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe current study aimed to propose a Deep Learning (DL) and Augmented Reality (AR) based solution for a in-vivo robot-assisted radical prostatectomy (RARP), to improve the precision of a published work from our group. We implemented a two-steps automatic system to align a 3D virtual ad-hoc model of a patient's organ with its 2D endoscopic image, to assist surgeons during the procedure.
methodsThis approach was carried out using a Convolutional Neural Network (CNN) based structure for semantic segmentation and a subsequent elaboration of the obtained output, which produced the needed parameters for attaching the 3D model. We used a dataset obtained from 5 endoscopic videos (A, B, C, D, E), selected and tagged by our team's specialists. We then evaluated the most performing couple of segmentation architecture and neural network and tested the overlay performances.
resultsU-Net stood out as the most effecting architectures for segmentation. ResNet and MobileNet obtained similar Intersection over Unit (IoU) results but MobileNet was able to elaborate almost twice operations per seconds. This segmentation technique outperformed the results from the former work, obtaining an average IoU for the catheter of 0.894 (σ = 0.076) compared to 0.339 (σ = 0.195). This modifications lead to an improvement also in the 3D overlay performances, in particular in the Euclidean Distance between the predicted and actual model's anchor point, from 12.569 (σ= 4.456) to 4.160 (σ = 1.448) and in the Geodesic Distance between the predicted and actual model's rotations, from 0.266 (σ = 0.131) to 0.169 (σ = 0.073).
conclusionThis work is a further step through the adoption of DL and AR in the surgery domain. In future works, we will overcome the limits of this approach and finally improve every step of the surgical procedure.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.