Automatic segmentation of fibroglandular tissue in breast MRI using anatomy-driven three-dimensional spatial context

2018 ◽  
Author(s):  
Susan Weinstein ◽  
Meng-Kang Hsieh ◽  
Lauren Pantalone ◽  
Despina Kontos ◽  
Dong Wei
Keyword(s):  
Automatic Segmentation ◽  
Three Dimensional ◽  
Breast Mri ◽  
Spatial Context ◽  
Fibroglandular Tissue

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.

Variable spatiotemporal resolution three-dimensional dixon sequence for rapid dynamic contrast-enhanced breast MRI

2014 ◽  
Vol 40 (6) ◽  
pp. spcone-spcone
Author(s):  
Manojkumar Saranathan ◽  
Dan W. Rettmann ◽  
Brian A. Hargreaves ◽  
Jafi A. Lipson ◽  
Bruce L. Daniel
Keyword(s):  
Three Dimensional ◽  
Breast Mri ◽  
Spatiotemporal Resolution ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced

scholarly journals A computationally efficient approach to segmentation of the aorta and coronary arteries using deep learning

2021 ◽  
Author(s):  
Wing Keung Cheung ◽  
Robert Bell ◽  
Arjun Nair ◽  
Leon Menezies ◽  
Riyaz Patel ◽  
...  
Keyword(s):  
Deep Learning ◽  
Coronary Arteries ◽  
Automatic Segmentation ◽  
Three Dimensional ◽  
Regions Of Interest ◽  
Dice Similarity Coefficient ◽  
Processing Unit ◽  
Two Dimensional ◽  
Computed Tomography Images ◽  
Graphical Processing

AbstractA fully automatic two-dimensional Unet model is proposed to segment aorta and coronary arteries in computed tomography images. Two models are trained to segment two regions of interest, (1) the aorta and the coronary arteries or (2) the coronary arteries alone. Our method achieves 91.20% and 88.80% dice similarity coefficient accuracy on regions of interest 1 and 2 respectively. Compared with a semi-automatic segmentation method, our model performs better when segmenting the coronary arteries alone. The performance of the proposed method is comparable to existing published two-dimensional or three-dimensional deep learning models. Furthermore, the algorithmic and graphical processing unit memory efficiencies are maintained such that the model can be deployed within hospital computer networks where graphical processing units are typically not available.

Clinical applicability and relevance of fibroglandular tissue segmentation on routine T1 weighted breast MRI

2017 ◽  
Vol 42 ◽  
pp. 119-125 ◽  
Author(s):  
Akshat C. Pujara ◽  
Artem Mikheev ◽  
Henry Rusinek ◽  
Harikrishna Rallapalli ◽  
Jerzy Walczyk ◽  
...  
Keyword(s):  
Breast Mri ◽  
Tissue Segmentation ◽  
Fibroglandular Tissue ◽  
Clinical Applicability

Impact of menopausal status on background parenchymal enhancement and fibroglandular tissue on breast MRI

2012 ◽  
Vol 22 (12) ◽  
pp. 2641-2647 ◽  
Author(s):  
Valencia King ◽  
Yajia Gu ◽  
Jennifer B. Kaplan ◽  
Jennifer D. Brooks ◽  
Malcolm C. Pike ◽  
...  
Keyword(s):  
Menopausal Status ◽  
Breast Mri ◽  
Fibroglandular Tissue ◽  
Background Parenchymal Enhancement ◽  
Parenchymal Enhancement

scholarly journals Automatic segmentation of puborectalis muscle on three‐dimensional transperineal ultrasound

2018 ◽  
Vol 52 (1) ◽  
pp. 97-102 ◽  
Author(s):  
F. van den Noort ◽  
A. T. M. Grob ◽  
C. H. Slump ◽  
C. H. van der Vaart ◽  
M. van Stralen
Keyword(s):  
Automatic Segmentation ◽  
Three Dimensional ◽  
Transperineal Ultrasound ◽  
Puborectalis Muscle
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