A Survey on Statistical Modeling and Machine Learning Approaches to Computer Assisted Medical Intervention: Intraoperative Anatomy Modeling and Optimization of Interventional Procedures

Morooka, Ken’ichi; Nakamoto, Masahiko; Sato, Yoshinobu

doi:10.1587/transinf.e96.d.784

Cited by 10 publications

(3 citation statements)

References 119 publications

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“…To date, approaches for US-based model completion mostly rely in SSMs for shape generation, but differ in the methods for building the SSM and in methods for adapting it to US data. Morooka et al and Sarkalkan et al [62,63] give an overview on US-based model completion; for a survey and comprehensible introduction we refer to [64]. Hacihaliloglu et al propose a statistical model of shape and pose of the spine and fit it to local phase features extracted from 3D ultrasound data for reconstruction.…”

Section: B Reconstructionmentioning

confidence: 99%

Knee Bone Models From Ultrasound

Hohlmann

Brößner²,

Phlippen³

et al. 2023

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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The number of total knee arthroplasties performed world-wide are on a rise. Patient-specific planning and implants may improve surgical outcomes, but require 3D models of the bones involved. Ultrasound may become a cheap and non-harmful imaging modality, if the short comings of segmentation techniques in terms of automation, accuracy and robustness are overcome. Furthermore, any kind of ultrasound-based bone reconstruction must involve some kind of model completion, in order to handle occluded areas, e.g., the frontal femur. A fully-automatic and robust processing pipeline is proposed, generating full bone models from 3D freehand ultrasound scanning. A convolutional neural network is combined with a statistical shape model to segment and extrapolate the bone surface. We evaluate the method in-vivo on 10 subjects, comparing the ultrasound-based model to a magnetic resonance imaging (MRI) reference. The partial freehand 3D record of the femur and tibia bones deviate by 0.7 to 0.8mm from the MRI reference. After completion and on average, the full bone model shows sub-millimetric error in case of the femur and 1.24mm in case of the tibia. Processing of the images is performed in real-time, and the final model fitting step is computed in less than one minute. On average, it took 22 minutes for a full record per subject.

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Section: B Reconstructionmentioning

confidence: 99%

Knee Bone Models From Ultrasound

Hohlmann

Brößner²,

Phlippen³

et al. 2023

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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“…In contrast, deformation was simulated in one study while applying force to an organ using a neural network [20], [21], [22], as a method for estimating deformation based on accumulated deformation data and machine learning. In a study by Morooka et al, deformation data for a deformed object were obtained beforehand by applying many types of force using the finite element method, so that the relation between the external force applied to the organ and the compressed vertex coordinates of the mesh could be learned by a neural network.…”

Section: Related Workmentioning

confidence: 99%

Deformation estimation of an elastic object by partial observation using a neural network

Yamamoto,

Nakao,

Ohzeki

et al. 2017

Preprint

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Deformation estimation of elastic object assuming an internal organ is important for the computer navigation of surgery. The aim of this study is to estimate the deformation of an entire three-dimensional elastic object using displacement information of very few observation points. A learning approach with a neural network was introduced to estimate the entire deformation of an object. We applied our method to two elastic objects; a rectangular parallelepiped model, and a human liver model reconstructed from computed tomography data. The average estimation error for the human liver model was 0.041 mm when the object was deformed up to 66.4 mm, from only around 3 % observations. These results indicate that the deformation of an entire elastic object can be estimated with an acceptable level of error from limited observations by applying a trained neural network to a new deformation.

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“…The fitting process aligns and deforms the statistical shape model to fit the sparse landmark vertices. Therefore the model-based approach is widely accepted due to their ability to effectively represent objects (Morooka, Nakamoto et al 2013). …”

Section: Introductionmentioning

confidence: 99%

Towards Multiple 3D Bone Surface Identification and Reconstruction Using Few 2D X-Ray Images for Intraoperative Applications

Prakoonwit

2014

International Journal of Art, Culture, Design, and Technology

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This article discusses a possible method to use a small number, e.g. 5, of conventional 2D X-ray images to reconstruct multiple 3D bone surfaces intraoperatively. Each bone's edge contours in X-ray images are automatically identified. Sparse 3D landmark points of each bone are automatically reconstructed by pairing the 2D X-ray images. The reconstructed landmark point distribution on a surface is approximately optimal covering main characteristics of the surface. A statistical shape model, dense point distribution model (DPDM), is then used to fit the reconstructed optimal landmarks vertices to reconstruct a full surface of each bone separately. The reconstructed surfaces can then be visualised and manipulated by surgeons or used by surgical robotic systems.

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A Survey on Statistical Modeling and Machine Learning Approaches to Computer Assisted Medical Intervention: Intraoperative Anatomy Modeling and Optimization of Interventional Procedures

Cited by 10 publications

References 119 publications

Knee Bone Models From Ultrasound

Knee Bone Models From Ultrasound

Deformation estimation of an elastic object by partial observation using a neural network

Towards Multiple 3D Bone Surface Identification and Reconstruction Using Few 2D X-Ray Images for Intraoperative Applications

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