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Items: 1 to 20 of 117

1.

Automatic Breast and Fibroglandular Tissue Segmentation in Breast MRI Using Deep Learning by a Fully-Convolutional Residual Neural Network U-Net.

Zhang Y, Chen JH, Chang KT, Park VY, Kim MJ, Chan S, Chang P, Chow D, Luk A, Kwong T, Su MY.

Acad Radiol. 2019 Jan 31. pii: S1076-6332(19)30036-4. doi: 10.1016/j.acra.2019.01.012. [Epub ahead of print]

PMID:
30713130
2.

Using deep learning to segment breast and fibroglandular tissue in MRI volumes.

Dalmış MU, Litjens G, Holland K, Setio A, Mann R, Karssemeijer N, Gubern-Mérida A.

Med Phys. 2017 Feb;44(2):533-546. doi: 10.1002/mp.12079.

PMID:
28035663
3.

Automated fibroglandular tissue segmentation and volumetric density estimation in breast MRI using an atlas-aided fuzzy C-means method.

Wu S, Weinstein SP, Conant EF, Kontos D.

Med Phys. 2013 Dec;40(12):122302. doi: 10.1118/1.4829496.

4.

Fully Automated Convolutional Neural Network Method for Quantification of Breast MRI Fibroglandular Tissue and Background Parenchymal Enhancement.

Ha R, Chang P, Mema E, Mutasa S, Karcich J, Wynn RT, Liu MZ, Jambawalikar S.

J Digit Imaging. 2019 Feb;32(1):141-147. doi: 10.1007/s10278-018-0114-7.

PMID:
30076489
5.

An investigation of the effect of fat suppression and dimensionality on the accuracy of breast MRI segmentation using U-nets.

Fashandi H, Kuling G, Lu Y, Wu H, Martel AL.

Med Phys. 2019 Mar;46(3):1230-1244. doi: 10.1002/mp.13375. Epub 2019 Feb 4.

PMID:
30609062
6.

Automated mammographic breast density estimation using a fully convolutional network.

Lee J, Nishikawa RM.

Med Phys. 2018 Mar;45(3):1178-1190. doi: 10.1002/mp.12763. Epub 2018 Feb 19.

PMID:
29363774
7.

Knowledge-based and deep learning-based automated chest wall segmentation in Magnetic Resonance Images of extremely dense breasts.

Verburg E, Wolterink JM, de Waard SN, Išgum I, van Gils CH, Veldhuis WB, Gilhuijs KGA.

Med Phys. 2019 Jul 5. doi: 10.1002/mp.13699. [Epub ahead of print]

PMID:
31274194
8.

Fully Automated Lung Lobe Segmentation in Volumetric Chest CT with 3D U-Net: Validation with Intra- and Extra-Datasets.

Park J, Yun J, Kim N, Park B, Cho Y, Park HJ, Song M, Lee M, Seo JB.

J Digit Imaging. 2019 May 31. doi: 10.1007/s10278-019-00223-1. [Epub ahead of print]

PMID:
31152273
9.

Fully automated segmentation of whole breast using dynamic programming in dynamic contrast enhanced MR images.

Jiang L, Hu X, Xiao Q, Gu Y, Li Q.

Med Phys. 2017 Jun;44(6):2400-2414. doi: 10.1002/mp.12254. Epub 2017 May 4.

PMID:
28375584
10.

Automated deep learning method for whole-breast segmentation in diffusion-weighted breast MRI.

Zhang L, Mohamed AA, Chai R, Guo Y, Zheng B, Wu S.

J Magn Reson Imaging. 2019 Jul 13. doi: 10.1002/jmri.26860. [Epub ahead of print]

PMID:
31301201
11.

Quantitative Volumetric K-Means Cluster Segmentation of Fibroglandular Tissue and Skin in Breast MRI.

Niukkanen A, Arponen O, Nykänen A, Masarwah A, Sutela A, Liimatainen T, Vanninen R, Sudah M.

J Digit Imaging. 2018 Aug;31(4):425-434. doi: 10.1007/s10278-017-0031-1.

PMID:
29047034
12.

Shading artifact correction in breast CT using an interleaved deep learning segmentation and maximum-likelihood polynomial fitting approach.

Ghazi P, Hernandez AM, Abbey C, Yang K, Boone JM.

Med Phys. 2019 May 18. doi: 10.1002/mp.13599. [Epub ahead of print]

PMID:
31102462
13.

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

Pujara AC, Mikheev A, Rusinek H, Rallapalli H, Walczyk J, Gao Y, Chhor C, Pysarenko K, Babb JS, Melsaether AN.

Clin Imaging. 2017 Mar - Apr;42:119-125. doi: 10.1016/j.clinimag.2016.12.002. Epub 2016 Dec 6.

PMID:
27951458
14.

Fully Automated Segmentation of Lower Extremity Deep Vein Thrombosis Using Convolutional Neural Network.

Huang C, Tian J, Yuan C, Zeng P, He X, Chen H, Huang Y, Huang B.

Biomed Res Int. 2019 Jun 9;2019:3401683. doi: 10.1155/2019/3401683. eCollection 2019.

15.
16.

Quantitative 3D breast magnetic resonance imaging fibroglandular tissue analysis and correlation with qualitative assessments: a feasibility study.

Ha R, Mema E, Guo X, Mango V, Desperito E, Ha J, Wynn R, Zhao B.

Quant Imaging Med Surg. 2016 Apr;6(2):144-50. doi: 10.21037/qims.2016.03.03.

17.

Breast Region Segmentation being Convolutional Neural Network in Dynamic Contrast Enhanced MRI.

Xu X, Fu L, Chen Y, Larsson R, Zhang D, Suo S, Hua J, Zhao J.

Conf Proc IEEE Eng Med Biol Soc. 2018 Jul;2018:750-753. doi: 10.1109/EMBC.2018.8512422.

PMID:
30440504
18.

Accuracy of fully automated, quantitative, volumetric measurement of the amount of fibroglandular breast tissue using MRI: correlation with anthropomorphic breast phantoms.

Wengert GJ, Pinker K, Helbich TH, Vogl WD, Spijker SM, Bickel H, Polanec SH, Baltzer PA.

NMR Biomed. 2017 Jun;30(6). doi: 10.1002/nbm.3705. Epub 2017 Mar 10.

PMID:
28295818
19.

Cross-modality (CT-MRI) prior augmented deep learning for robust lung tumor segmentation from small MR datasets.

Jiang J, Hu YC, Tyagi N, Zhang P, Rimner A, Deasy JO, Veeraraghavan H.

Med Phys. 2019 Jul 5. doi: 10.1002/mp.13695. [Epub ahead of print]

PMID:
31274206
20.

Fully automatic multi-organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks.

Tong N, Gou S, Yang S, Ruan D, Sheng K.

Med Phys. 2018 Oct;45(10):4558-4567. doi: 10.1002/mp.13147. Epub 2018 Sep 19.

PMID:
30136285

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