Automated Image Processing Based 3D Printed Scaffolds For Critical Size Bone Fracture Treatment
文献类型： 会议论文
第一作者： Abrar Hussain Syed
作者： Abrar Hussain Syed 1 ; Ahmadreza Baghaie 2 ; Azhar Ilyas 3 ;
作者机构： 1.Dept. of Computing Science, Bio-Nanotechnology and Biomaterials (BNB) Lab, New York Institute of Technology, Old Westbury, NY
2.Dept. of Electrical and Computer Engineering, Applied Signal & Image Processing (ASIP) Lab, New York Institute of Technology, Old Westbury, NY
3.Dept. of Electrical and Computer Engineering, Bio-Nanotechnology and Biomaterials (BNB) Lab, New York Institute of Technology, Old Westbury, NY
关键词： Training;Image segmentation;Solid modeling;Three-dimensional displays;Thresholding (Imaging);Computed tomography;Simulated annealing
会议名称： IEEE Long Island Systems, Applications and Technology Conference
主办单位：
页码： 1-6
摘要： Bone tissues in critical size bone fracture cases do not re-generate naturally and require special treatment procedures, such as cast alignments, support plates and bone grafting. These procedures are risky and often have high rejection rates. Modern clinical procedures use scaffold implants that can facilitate the process of healing with a lesser risk, provide mechanical support and a porous, nutritious medium for bone tissue regeneration and recovery. They require special training, tools, and significant time to be manufactured, and are generally made at a dedicated laboratory. The whole process takes around a week to be manufactured and implanted in the fracture site. In this work a novel technique for automatic segmentation of bone fractures from CT scan images to facilitate the process of manufacturing the patient-specific scaffolds in a significantly shorter time, without the need of skilled personnel has been presented. To achieve this, the procedure of generating 3D printable models was automated using image processing and machine learning algorithms. For this, 3D CT (Computed Tomography) images were used as input (as a series of 2D slices), acquired using a micro-CT scanner for the approach. After pre-processing the acquired images (filtering), thresholding segmentation was applied to extract the bone from the scan. This step is followed by orientation optimization of the segmentation result, by taking advantage of a global optimization technique, namely Simulated Annealing, to ensure maximized visibility of the fracture in a projected view of the volume by projecting the volume on a 2D surface. Binary hole-filling techniques and bone thickness estimation is then used to create a 3D template (model) to be sent for scaffold printing to a compatible 3D printer. Experiments with both synthetic and real datasets show that the proposed method is an effective approach for creating rapid, precise, and patient-specific 3D scaffolds to treat critical-size bone fractures.
分类号： tp3-53
- 相关文献
[1]Unsupervised body part regression via spatially self-ordering convolutional neural networks. Ke Yan,Le Lu,Ronald M. Summers. 2018
[2]Automatic Multi-Atlas Liver Segmentation and Couinaud Classification from CT Volumes. Sofia Pla-Alemany,Juan Antonio Romero,Jose Manuel Santabarbara,Roberto Aliaga,Alicia M. Maceira,David Moratal. 2021
[3]A Modified Protocol to Evaluate Masseter Morphology Based on Automatic Segmentation and Registration. Ling Wu,Jing Wang,Xiao-Jing Liu. 2022
[4]Improving Automatic Segmentation of lymphoma with Additional Medical Knowledge Priors. Liangchen Liu,Benjamin Hou,Manas Kumar Nag,Jianfei Liu,Navid Hasani,Thomas C. Shen,Yingying Zhu,Babak Saboury,Jing Xiao,Ronald M. Summers. 2023
[5]XNet: Wavelet-Based Low and High Frequency Fusion Networks for Fully- and Semi-Supervised Semantic Segmentation of Biomedical Images. Yanfeng Zhou,Jiaxing Huang,Chenlong Wang,Le Song,Ge Yang. 2023
[6]Cascaded coarse-to-fine convolutional neural networks for pericardial effusion localization and segmentation on CT scans. Jiamin Liu,Jinzheng Cai,Karthik Chellamuthu,Mohammadhadi Bagheri,Le Lu,Ronald M. Summers. 2018
[7]Effects of Acetylcholine Release Spatial Distribution on the Frequency of Atrial Reentrant Circuits: a Computational Study. Chiara Celotto,Carlos Sánchez,Mostafa Abdollahpur,Frida Sandberg,Jose F Rodriguez,Pablo Laguna,Esther Pueyo. 2022
[8]A multi-level convolutional LSTM model for the segmentation of left ventricle myocardium in infarcted porcine cine MR images. Dongqing Zhang,Ilknur Icke,Belma Dogdas,Sarayu Parimal,Smita Sampath,Joseph Forbes,Ansuman Bagchi,Chih-Liang Chin,Antong Chen. 2018
[9]Automatic Tool for Pulmonary Artery Hemodynamic Assessment from 4D flow MRI. Maialen Stephens,ángel Gaitán,Rahul Kumar,Jorge Nuche,Irene Marco,Juan Delgado,Jesús Ruíz-Cabello,Karen López-Linares,Javier Bóbeda,Haizea Erostarbe. 2023
[10]VolRAFT: Volumetric Optical Flow Network for Digital Volume Correlation of Synchrotron Radiation-based Micro-CT Images of Bone-Implant Interfaces. Tak Ming Wong,Julian Moosmann,Berit Zeller-Plumhoff. 2024
[11]Image segmentation of G bands of Triticum monococcum chromosomes based on the model-based neural network. Cai, N,Hu, KH,Xiong, HT,Li, SY,Su, WF,Zhu, FS.
[12]Robust Image Segmentation Method for Cotton Leaf Under Natural Conditions Based on Immune Algorithm and PCNN Algorithm. Zhang, Jianhua,Kong, Fantao,Wu, Jianzhai,Han, Shuqing,Zhai, Zhifen. 2018
[13]Image Segmentation to HSI Model Based on Improved Particle Swarm Optimization. Zhao, Bo,Mao, Wenhua,Zhang, Xiaochao,Chen, Yajun. 2009
[14]A segmentation method for greenhouse vegetable foliar disease spots images using color information and region growing. Ma, Juncheng,Du, Keming,Zheng, Feixiang,Chu, Jinxiang,Sun, Zhongfu,Zhang, Lingxian.
[15]Efficient Biochemical Pathway Prediction Using Simulated Annealing. Zhiqing Xu,Radhakrishnan Mahadevan. 2018
[16]Multivariable Regression Model to Estimate Tidal Volume for Different Respiratory Patterns. Daniel Romero Pérez,Jordi Solà Soler,Leon Balchin,Arantxa Mas Serra,Manuel Lujan Torné,Melinda R. Popoviciu Koborzan,Beatriz F. Giraldo. 2023
[17]A computer aided for image processing of computed tomography in hepatocellular carcinoma. Hsu, Wei-Tai,Yeh, Jia-Rong,Chang, Yi-Chung,Lo, Men-Tzung,Lin, Yi-Hsien. 2011
[18]A Software Tool for the Measurement of the Aortic Annulus Area by Means of Computed Tomography Image Analysis for the Planning of Transcatheter Aortic Valve Replacement (TAVR). Gloria Campos Hervás,Agustín Fernández Cisnal,Sergio García-Blas,Gema Mi?ana,Ernesto Valero,Juan Sanchis,David Moratal. 2023
[19]Exploring the Impact of Acquisition and Reconstruction Parameters on an Imaging-Based Lung Cancer Risk Model. Luoting Zhuang,Anil Yadav,Grace H Kim,Seyed Mohammad Hossein Tabatabaei,Ashley Prosper,William Hsu. 2024
[20]Image Similarity Ranking of Focal Computed Tomography Liver Lesions Using a 2AFC Technique. Jessica Faruque,Sameer Antani,Rodney Long,Lauren Kim,George Thoma. 2016
作者其他论文 更多>>
-
Effect of Silica nanoparticles (Laponite) on 3D printed Gelatin Methacryloyl Cell-based Scaffolds
作者：Naomi Edwards;Shebin Tharakan;Michael Hadjiargyrou;Azhar Ilyas
关键词：Silicon compounds;Nanoparticles;Three-dimensional displays;Fluids;Tissue engineering;Surgery;Bones
