A complete-pelvis segmentation framework for image-free total hip arthroplasty (THA): methodology and clinical study

Xie, Weiguo; Franke, Jochen; Chen, Cheng; Grützner, Paul Alfred; Schumann, Steffen; Nolte, Lutz‐Peter; Zheng, Guoyan

doi:10.1002/rcs.1619

Cited by 6 publications

(6 citation statements)

References 44 publications

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Section: Resultsmentioning

confidence: 99%

“…All recent applications of SSAMs to automatic segmentation were either targeted to vertebrae [ 51 , 53 , 55 ] or to hip or full lower limb [ 49 , 50 , 52 , 54 ]. Interestingly, all studies except one [ 54 ] focused on segmentation of computed tomography (CT) images, despite CT not being commonly adopted in clinical care of osteoporosis. All methods are automatic or semi-automatic, where human interaction typically consists in manually picking some anatomical landmarks on the target image [ 54 ].…”

Section: Resultsmentioning

confidence: 99%

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Statistical Shape and Appearance Models: Development Towards Improved Osteoporosis Care

Grassi

Väänänen

Isaksson

2021

Curr Osteoporos Rep

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Purpose of Review Statistical models of shape and appearance have increased their popularity since the 1990s and are today highly prevalent in the field of medical image analysis. In this article, we review the recent literature about how statistical models have been applied in the context of osteoporosis and fracture risk estimation. Recent Findings Recent developments have increased their ability to accurately segment bones, as well as to perform 3D reconstruction and classify bone anatomies, all features of high interest in the field of osteoporosis and fragility fractures diagnosis, prevention, and treatment. An increasing number of studies used statistical models to estimate fracture risk in retrospective case-control cohorts, which is a promising step towards future clinical application. Summary All the reviewed application areas made considerable steps forward in the past 5–6 years. Heterogeneities in validation hinder a thorough comparison between the different methods and represent one of the future challenges to be addressed to reach clinical implementation.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Statistical Shape and Appearance Models: Development Towards Improved Osteoporosis Care

Grassi

Väänänen

Isaksson

2021

Curr Osteoporos Rep

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“…La tarea de detección de la fractura puede ser difícil debido a la baja resolución de las imágenes de Rx, solapamiento de huesos, variaciones en la forma ósea y diferencia entre cada paciente [14]. Considerando además que la presencia de ruido, efectos de bajo contraste y nitidez de las imágenes de Rx, hacen que la tarea de detección de fracturas de acetábulo de forma automatizada sea desafiante, debido a que contiene información que no se puede analizar a fondo con la inspección visual.…”

Section: Lesiones Traumatológicasunclassified

Implementación de un Modelo basado en técnicas de Deep Learning aplicado a la Visión Computacional en la Clasificación de Imágenes de Rayos X, para el soporte del diagnóstico de lesiones traumatológicas de la Estructura Pélvica

Lopez¹,

Gutierrez²

2019

Proceedings of the 17th LACCEI International Multi-Conference for Engineering, Education, and Technology: “Industry, Innovation

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This article describes the implementation of a model based on Deep Learning techniques applied to computational vision in the activity of classifying X-ray images as support in the diagnosis of traumatic lesions of the pelvic structure, specifically the acetabulum of the pelvis. In the area of Medical Sciences, nowadays, it is essential to have automated tools that support medical diagnosis. For the construction of these tools it is necessary to analyze the different techniques or methods provided by Computing, specifically Deep Learning, for the processing and interpretation of images and potentialize them with the application of GPUs to accelerate the achievement of results.

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“…In Xie et al, anatomical landmarks were a prerequisite for the segmentation of the pelvis in anterior-posterior radiographs in order to create a 3D patient specific pelvis model for surgical planning. The shape model utilized for this purpose is based on anatomical landmarks [30].…”

Section: Introductionmentioning

confidence: 99%

Learning to detect anatomical landmarks of the pelvis in X-rays from arbitrary views

et al. 2019

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Purpose-Minimally invasive alternatives are now available for many complex surgeries. These approaches are enabled by the increasing availability of intra-operative image guidance. Yet, fluoroscopic X-rays suffer from projective transformation and thus cannot provide direct views onto anatomy. Surgeons could highly benefit from additional information, such as the anatomical landmark locations in the projections, to support intra-operative decision making. However, detecting landmarks is challenging since the viewing direction changes substantially between views leading to varying appearance of the same landmark. Therefore, and to the best of our knowledge, view-independent anatomical landmark detection has not been investigated yet. Methods-In this work, we propose a novel approach to detect multiple anatomical landmarks in X-ray images from arbitrary viewing directions. To this end, a sequential prediction framework based on convolutional neural networks is employed to simultaneously regress all landmark locations. For training, synthetic X-rays are generated with a physically accurate forward model that allows direct application of the trained model to real X-ray images of the pelvis. View invariance is achieved via data augmentation by sampling viewing angles on a spherical segment of 120° × 90°. Results-On synthetic data, a mean prediction error of 5.6 ± 4.5mm is achieved. Further, we demonstrate that the trained model can be directly applied to real X-rays and show that these detections define correspondences to a respective CT volume, which allows for analytic estimation of the 11 degree of freedom projective mapping.

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A complete-pelvis segmentation framework for image-free total hip arthroplasty (THA): methodology and clinical study

Abstract: Despite further validation needed, the preliminary results implied the underlying clinical applicability of the proposed framework for image-free THA.

Cited by 6 publications

References 44 publications

Statistical Shape and Appearance Models: Development Towards Improved Osteoporosis Care

Statistical Shape and Appearance Models: Development Towards Improved Osteoporosis Care

Implementación de un Modelo basado en técnicas de Deep Learning aplicado a la Visión Computacional en la Clasificación de Imágenes de Rayos X, para el soporte del diagnóstico de lesiones traumatológicas de la Estructura Pélvica

Learning to detect anatomical landmarks of the pelvis in X-rays from arbitrary views

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