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2 results on '"Manual reconstruction"'

1. Application of Artificial Intelligence to Automate the Reconstruction of Muscle Cross-Sectional Area Obtained by Ultrasound.

Author

GOMES DA SILVA, DEIVID, GOMES DA SILVA, DIEGO, ANGLERI, VITOR, CAMARGO SCARPELLI, MAÍRA, ALMEIDA BERGAMASCO, JOÃO GUILHERME, ROCHA NÓBREGA, SANMY, DAMAS, FELIPE, SANTOS CHAVES, TALISSON, DE ARRUDA CAMARGO, HELOISA, UGRINOWITSCH, CARLOS, and LIBARDI, CLEITON AUGUSTO

Subjects
*RESEARCH funding, *ARTIFICIAL intelligence, *RESEARCH methodology evaluation, *MAGNETIC resonance imaging, *DESCRIPTIVE statistics, *SKIN, *TENDONS, *ARTIFICIAL neural networks, *FASCIAE (Anatomy), *FEMUR, *AUTOMATION, *QUADRICEPS muscle, *DIGITAL image processing, *EVALUATION
Abstract

Purpose: Manual reconstruction (MR) of the vastus lateralis (VL) muscle cross-sectional area (CSA) from sequential ultrasound (US) images is accessible, is reproducible, and has concurrent validity with magnetic resonance imaging. However, this technique requires numerous controls and procedures during image acquisition and reconstruction, making it laborious and time-consuming. The aim of this study was to determine the concurrent validity of VL CSA assessments between MR and computer vision--based automated reconstruction (AR) of CSA from sequential images of the VL obtained by US. Methods: The images from each sequence were manually rotated to align the fascia between images and thus visualize the VL CSA. For the AR, an artificial neural network model was utilized to segment areas of interest in the image, such as skin, fascia, deep aponeurosis, and femur. This segmentation was crucial to impose necessary constraints for the main assembly phase. At this stage, an image registration application, combined with differential evolution, was employed to achieve appropriate adjustments between the images. Next, the VL CSA obtained from the MR (n = 488) and AR (n = 488) techniques was used to determine their concurrent validity. Results: Our findings demonstrated a low coefficient of variation (CV) (1.51%) for AR compared with MR. The Bland--Altman plot showed low bias and close limits of agreement (+1.18 cm², -1.19 cm²), containing more than 95% of the data points. Conclusions: The AR technique is valid compared with MR when measuring VL CSA in a heterogeneous sample. [ABSTRACT FROM AUTHOR]

Published
2024
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2. The use of computer vision for the automatized reconstruction of muscle cross-sectional area after the aquisition of sequential images obtained by ultrassonography

Author

Silva, Deivid Gomes da and Libardi, Cleiton Augusto

Subjects
Artificial intelligence, Automation, Automatização, Skeletal muscle, Manual reconstruction, Inteligência artificial, Reconstrução manual, EDUCACAO FISICA [CIENCIAS DA SAUDE], Músculo esquelético
Abstract

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) The aim of the present study was to propose and validate a tool based on computer vision techniques that allows for the automated reconstruction (AR) of the vastus lateralis (VL) muscular cross-sectional area (MCSA) from sequential images obtained using an ultrasound (US) machine. Methods: Four hundred and eighty-eight VL US image sequences were used for VL MCSA reconstruction. Two different reconstruction techniques were utilized. For the already validated manual reconstruction (MR) technique, the sequential images were manually adjusted until the MCSA of the VL was fully visible for each image sequence. For AR, computer vision techniques were combined in a tool capable of automatically reconstructing the MCSA of the VL based on the steps described for the proper application of MR. After the quantification in cm² of all VL MCSA by both MR (n = 488) and AR (n = 488) techniques, the results were used to investigate the validity of the AR in measuring VL MCSA from of sequential images of the VL obtained by US. Our findings demonstrated good validity with low coefficient of variation values (1.51%) for AR compared to MR. The Bland-Altman plot showed low bias (-0.01 cm²; IC95% = 0.04, -0.06) and close limits of agreement (+1.18 cm², -1.19 cm²) containing 95% of the comparisons. Conclusion: The AR technique is valid compared to MR when measuring VL MCSA in a heterogeneous sample. O objetivo do presente estudo foi propor e validar uma ferramenta baseada em técnicas de visão computacional que possibilite a reconstrução automatizada (RA) da área de secção transversa muscular (ASTM) a partir de imagens sequenciais do vasto lateral (VL) obtidas por meio de ultrassom (US). Métodos: quatrocentos e oitenta e oito sequências de imagens de US do VL foram utilizadas para reconstrução da ASTM do VL. Duas técnicas de reconstrução foram utilizadas. Para a já validada técnica de reconstrução manual (RM) as imagens sequenciais foram ajustadas manualmente até que a ASTM do VL fosse reconstruída para cada sequência de imagens. Para a RA, com base nos passos descritos para adequada aplicação da RM, técnicas de visão computacional foram combinadas em uma ferramenta capaz de reconstruir a ASTM do VL de maneira automática a partir de uma sequência de imagens do VL obtidas por US. Após a quantificação em cm² de todas as ASTM do VL obtidas pela técnica de RM (n = 488) e pela técnica de RA (n = 488), os resultados foram utilizados para investigarmos a validade da RA em mensurar a ASTM do VL. Nossos achados demonstraram boa validade com valores baixos de coeficiente de variação (1,51%) para RA em comparação com a RM. O gráfico de Bland-Altman apresentou baixo viés (-0,01 cm²; IC 95% = 0,04, -0.06) e limites de concordância próximos (+1,18cm², -1,19cm²) contendo 95% das comparações. Conclusão: A técnica de RA é válida para mensurar ASTM do VL em uma amostra heterogênea quando comparada a RM. 88882.426920/2019-01, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Published
2022

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