scholarly journals Three-dimensional ultrasound for knee osteophyte depiction: a comparative study to computed tomography

2021 ◽  
Author(s):  
Valeria Vendries ◽  
Tamas Ungi ◽  
Jordan Harry ◽  
Manuela Kunz ◽  
Jana Podlipská ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Automatic Segmentation ◽  
Three Dimensional ◽  
3D Ultrasound ◽  
Surgical Interventions ◽  
Structured Light Scanner ◽  
Joint Surfaces ◽  
Operative Planning ◽  
Rms Error ◽  
Tissue Characteristics

Abstract Purpose Osteophytes are common radiographic markers of osteoarthritis. However, they are not accurately depicted using conventional imaging, thus hampering surgical interventions that rely on pre-operative images. Studies have shown that ultrasound (US) is promising at detecting osteophytes and monitoring the progression of osteoarthritis. Furthermore, three-dimensional (3D) ultrasound reconstructions may offer a means to quantify osteophytes. The purpose of this study was to compare the accuracy of osteophyte depiction in the knee joint between 3D US and conventional computed tomography (CT). Methods Eleven human cadaveric knees were pre-screened for the presence of osteophytes. Three osteoarthritic knees were selected, and then, 3D US and CT images were obtained, segmented, and digitally reconstructed in 3D. After dissection, high-resolution structured light scanner (SLS) images of the joint surfaces were obtained. Surface matching and root mean square (RMS) error analyses of surface distances were performed to assess the accuracy of each modality in capturing osteophytes. The RMS errors were compared between 3D US, CT and SLS models. Results Average RMS error comparisons for 3D US versus SLS and CT versus SLS models were 0.87 mm ± 0.33 mm (average ± standard deviation) and 0.95 mm ± 0.32 mm, respectively. No statistical difference was found between 3D US and CT. Comparative observations of imaging modalities suggested that 3D US better depicted osteophytes with cartilage and fibrocartilage tissue characteristics compared to CT. Conclusion Using 3D US can improve the depiction of osteophytes with a cartilaginous portion compared to CT. It can also provide useful information about the presence and extent of osteophytes. Whilst algorithm improvements for automatic segmentation and registration of US are needed to provide a more robust investigation of osteophyte depiction accuracy, this investigation puts forward the potential application for 3D US in routine diagnostic evaluations and pre-operative planning of osteoarthritis.

Automated Pancreas Segmentation And Volumetry Using Deep Neural Network On Computed Tomography

2021 ◽  
Author(s):  
Sang-Heon Lim ◽  
Young Jae Kim ◽  
Yeon-Ho Park ◽  
Doojin Kim ◽  
Kwang Gi Kim ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Automatic Segmentation ◽  
External Validation ◽  
Three Dimensional ◽  
Semantic Segmentation ◽  
Quantitative Information ◽  
Similarity Coefficients ◽  
Internal Validation ◽  
Pancreas Segmentation

Abstract Pancreas segmentation is necessary for observing lesions, analyzing anatomical structures, and predicting patient prognosis. Therefore, various studies have designed segmentation models based on convolutional neural networks for pancreas segmentation. However, the deep learning approach is limited by a lack of data, and studies conducted on a large computed tomography dataset are scarce. Therefore, this study aims to perform deep-learning-based semantic segmentation on 1,006 participants and evaluate the automatic segmentation performance of the pancreas via four individual three-dimensional segmentation networks. In this study, we performed internal validation with 1,006 patients and external validation using the Cancer Imaging Archive (TCIA) pancreas dataset. We obtained mean precision, recall, and dice similarity coefficients of 0.869, 0.842, and 0.842, respectively, for internal validation via a relevant approach among the four deep learning networks. Using the external dataset, the deep learning network achieved mean precision, recall, and dice similarity coefficients of 0.779, 0.749, and 0.735, respectively. We expect that generalized deep-learning-based systems can assist clinical decisions by providing accurate pancreatic segmentation and quantitative information of the pancreas for abdominal computed tomography.

scholarly journals Craniosynostosis: prenatal diagnosis by 2D/3D ultrasound, magnetic resonance imaging and computed tomography.

2016 ◽  
Vol 18 (3) ◽  
pp. 378 ◽  
Author(s):  
Talita Micheletti Helfer ◽  
Alberto Borges Peixoto ◽  
Gabriele Tonni ◽  
Edward Araujo Júnior
Keyword(s):  
Magnetic Resonance Imaging ◽  
Computed Tomography ◽  
Prenatal Diagnosis ◽  
Magnetic Resonance ◽  
Three Dimensional ◽  
3D Ultrasound ◽  
Resonance Imaging ◽  
Perinatal Complications ◽  
Wide Range ◽  
Magnetic Resonance Imaging Mri

Craniosynostosis is defined as the process of premature fusion of one or more of the cranial sutures.  It is a common condition that occurs in about 1 to 2,000 live births. Craniosynostosis may be classified in primary or secondary. It is also classified as nonsyndromic or syndromic. According to suture commitment, craniosynostosis may affect a single suture or multiple sutures. There is a wide range of syndromes involving craniosynostosis and the most common are Apert, Pffeifer, Crouzon, Shaethre-Chotzen and Muenke syndromes. The underlying etiology of nonsyndromic craniosynostosis is unknown. Mutations in the fibroblast growth factor (FGF) signalling pathway play a crucial role in the etiology of craniosynostosis syndromes. Prenatal ultrasound`s detection rate of craniosynostosis is low. Nowadays, different methods can be applied for prenatal diagnosis of craniosynostosis, such as two-dimensional (2D) and three-dimensional (3D) ultrasound, magnetic resonance imaging (MRI), computed tomography (CT) scan and, finally, molecular diagnosis. The presence of craniosynostosis may affect the birthing process. Fetuses with craniosynostosis also have higher rates of perinatal complications. In order to avoid the risks of untreated craniosynostosis, children are usually treated surgically soon after postnatal diagnosis.

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