Related papers: A Semi-automatic Cranial Implant Design Tool Based on Rigid ICP Template Alignment and Voxel Space Reconstruction

A Semi-automatic Cranial Implant Design Tool Based on Rigid ICP Template Alignment and Voxel Space Reconstruction

URL: http://arxiv.org/abs/2404.15287v1
Date: Tue, 19 Mar 2024 08:24:05 GMT
Title: A Semi-automatic Cranial Implant Design Tool Based on Rigid ICP Template Alignment and Voxel Space Reconstruction
Authors: Michael Lackner, Behrus Puladi, Jens Kleesiek, Jan Egger, Jianning Li,
Abstract summary: cranioplasty is the craft of neurocranial repair using cranial implants. Despite the improvements made in recent years, the design of a patient-specific implant (PSI) is among the most complex, expensive, and least automated tasks in cranioplasty. We create a prototype application with a graphical user interface (UI) specifically tailored for semi-automatic implant generation. A general outline of the proposed implant generation process involves setting an area of interest, aligning the templates, and then creating the implant in voxel space.
Score: 2.0793077626669327
License: http://creativecommons.org/licenses/by/4.0/
Abstract: In traumatic medical emergencies, the patients heavily depend on cranioplasty - the craft of neurocranial repair using cranial implants. Despite the improvements made in recent years, the design of a patient-specific implant (PSI) is among the most complex, expensive, and least automated tasks in cranioplasty. Further research in this area is needed. Therefore, we created a prototype application with a graphical user interface (UI) specifically tailored for semi-automatic implant generation, where the users only need to perform high-level actions. A general outline of the proposed implant generation process involves setting an area of interest, aligning the templates, and then creating the implant in voxel space. Furthermore, we show that the alignment can be improved significantly, by only considering clipped geometry in the vicinity of the defect border. The software prototype will be open-sourced at https://github.com/3Descape/Cranial_Implant_Design

Related papers

Creating a Digital Twin of Spinal Surgery: A Proof of Concept [68.37190859183663]
Surgery digitalization is the process of creating a virtual replica of real-world surgery. We present a proof of concept (PoC) for surgery digitalization that is applied to an ex-vivo spinal surgery. We employ five RGB-D cameras for dynamic 3D reconstruction of the surgeon, a high-end camera for 3D reconstruction of the anatomy, an infrared stereo camera for surgical instrument tracking, and a laser scanner for 3D reconstruction of the operating room and data fusion.
arXiv Detail & Related papers (2024-03-25T13:09:40Z)
Fully automated workflow for the design of patient-specific orthopaedic implants: application to total knee arthroplasty [0.0]
The proposed workflow allows for a fast and reliable personalisation of knee implants, directly from the patient CT image. It establishes a patient-specific pre-operative planning for TKA in a very short time making it easily available for all patients. This solution could help answer the growing number of arthroplasties while reducing complications and improving the patients' satisfaction.
arXiv Detail & Related papers (2024-03-22T17:08:03Z)
Neural LerPlane Representations for Fast 4D Reconstruction of Deformable Tissues [52.886545681833596]
LerPlane is a novel method for fast and accurate reconstruction of surgical scenes under a single-viewpoint setting. LerPlane treats surgical procedures as 4D volumes and factorizes them into explicit 2D planes of static and dynamic fields. LerPlane shares static fields, significantly reducing the workload of dynamic tissue modeling.
arXiv Detail & Related papers (2023-05-31T14:38:35Z)
Two-Stream Regression Network for Dental Implant Position Prediction [29.864052654136614]
In implant prosthesis treatment, the design of the surgical guide heavily relies on the manual location of the implant position. Deep learning based methods has started to be applied to address this problem. The space between teeth are various and some of them might present similar texture characteristic with the actual implant region.
arXiv Detail & Related papers (2023-05-17T08:37:26Z)
Safe Deep RL for Intraoperative Planning of Pedicle Screw Placement [61.28459114068828]
We propose an intraoperative planning approach for robotic spine surgery that leverages real-time observation for drill path planning based on Safe Deep Reinforcement Learning (DRL) Our approach was capable of achieving 90% bone penetration with respect to the gold standard (GS) drill planning.
arXiv Detail & Related papers (2023-05-09T11:42:53Z)
Robotic Navigation Autonomy for Subretinal Injection via Intelligent Real-Time Virtual iOCT Volume Slicing [88.99939660183881]
We propose a framework for autonomous robotic navigation for subretinal injection. Our method consists of an instrument pose estimation method, an online registration between the robotic and the i OCT system, and trajectory planning tailored for navigation to an injection target. Our experiments on ex-vivo porcine eyes demonstrate the precision and repeatability of the method.
arXiv Detail & Related papers (2023-01-17T21:41:21Z)
ImplantFormer: Vision Transformer based Implant Position Regression Using Dental CBCT Data [27.020346431680355]
Implant prosthesis is the most appropriate treatment for dentition defect or dentition loss, which usually involves a surgical guide design process to decide the implant position. In this paper, a transformer-based Implant Position Regression Network, ImplantFormer, is proposed to automatically predict the implant position based on the oral CBCT data. We creatively propose to predict the implant position using the 2D axial view of the tooth crown area and fit a centerline of the implant to obtain the actual implant position at the tooth root.
arXiv Detail & Related papers (2022-10-29T02:31:27Z)
Back to the Roots: Reconstructing Large and Complex Cranial Defects using an Image-based Statistical Shape Model [0.636460243469043]
A statistical shape model (SSM) built directly on the segmentation masks of the skulls is presented. We evaluate the SSM on several cranial implant design tasks, and the results show that it is capable of reconstructing large and complex defects with only minor manual corrections. In contrast, CNN-based approaches, even with massive data augmentation, fail or produce less-than-satisfactory implants for these cases.
arXiv Detail & Related papers (2022-04-12T10:58:05Z)
A unified 3D framework for Organs at Risk Localization and Segmentation for Radiation Therapy Planning [56.52933974838905]
Current medical workflow requires manual delineation of organs-at-risk (OAR) In this work, we aim to introduce a unified 3D pipeline for OAR localization-segmentation. Our proposed framework fully enables the exploitation of 3D context information inherent in medical imaging.
arXiv Detail & Related papers (2022-03-01T17:08:41Z)
A Self-Supervised Deep Framework for Reference Bony Shape Estimation in Orthognathic Surgical Planning [55.30223654196882]
A virtual orthognathic surgical planning involves simulating surgical corrections of jaw deformities on 3D facial bony shape models. A reference facial bony shape model representing normal anatomies can provide an objective guidance to improve planning accuracy. We propose a self-supervised deep framework to automatically estimate reference facial bony shape models.
arXiv Detail & Related papers (2021-09-11T05:24:40Z)
An Online Platform for Automatic Skull Defect Restoration and Cranial Implant Design [0.5551220224568872]
The system automatically restores the missing part of a skull and generates the desired implant. The generated implant can be downloaded in the STereoLithography (.stl) format directly via the browser interface of the system. The implant model can then be sent to a 3D printer for in loco implant manufacturing.
arXiv Detail & Related papers (2020-06-01T14:41:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.