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Items: 1 to 20 of 100

1.

Ensemble of Convolutional Neural Networks Improves Automated Segmentation of Acute Ischemic Lesions Using Multiparametric Diffusion-Weighted MRI.

Winzeck S, Mocking SJT, Bezerra R, Bouts MJRJ, McIntosh EC, Diwan I, Garg P, Chutinet A, Kimberly WT, Copen WA, Schaefer PW, Ay H, Singhal AB, Kamnitsas K, Glocker B, Sorensen AG, Wu O.

AJNR Am J Neuroradiol. 2019 Jun;40(6):938-945. doi: 10.3174/ajnr.A6077. Epub 2019 May 30.

PMID:
31147354
2.

Big Data Approaches to Phenotyping Acute Ischemic Stroke Using Automated Lesion Segmentation of Multi-Center Magnetic Resonance Imaging Data.

Wu O, Winzeck S, Giese AK, Hancock BL, Etherton MR, Bouts MJRJ, Donahue K, Schirmer MD, Irie RE, Mocking SJT, McIntosh EC, Bezerra R, Kamnitsas K, Frid P, Wasselius J, Cole JW, Xu H, Holmegaard L, Jiménez-Conde J, Lemmens R, Lorentzen E, McArdle PF, Meschia JF, Roquer J, Rundek T, Sacco RL, Schmidt R, Sharma P, Slowik A, Stanne TM, Thijs V, Vagal A, Woo D, Bevan S, Kittner SJ, Mitchell BD, Rosand J, Worrall BB, Jern C, Lindgren AG, Maguire J, Rost NS.

Stroke. 2019 Jul;50(7):1734-1741. doi: 10.1161/STROKEAHA.119.025373. Epub 2019 Jun 10.

PMID:
31177973
3.

Evaluation of Diffusion Lesion Volume Measurements in Acute Ischemic Stroke Using Encoder-Decoder Convolutional Network.

Kim YC, Lee JE, Yu I, Song HN, Baek IY, Seong JK, Jeong HG, Kim BJ, Nam HS, Chung JW, Bang OY, Kim GM, Seo WK.

Stroke. 2019 Jun;50(6):1444-1451. doi: 10.1161/STROKEAHA.118.024261. Epub 2019 May 16.

PMID:
31092169
4.

Fully automatic, multiorgan segmentation in normal whole body magnetic resonance imaging (MRI), using classification forests (CFs), convolutional neural networks (CNNs), and a multi-atlas (MA) approach.

Lavdas I, Glocker B, Kamnitsas K, Rueckert D, Mair H, Sandhu A, Taylor SA, Aboagye EO, Rockall AG.

Med Phys. 2017 Oct;44(10):5210-5220. doi: 10.1002/mp.12492. Epub 2017 Aug 31.

5.

Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks.

Chen L, Bentley P, Rueckert D.

Neuroimage Clin. 2017 Jun 13;15:633-643. doi: 10.1016/j.nicl.2017.06.016. eCollection 2017.

6.

Ischemic lesion volume determination on diffusion weighted images vs. apparent diffusion coefficient maps.

Bråtane BT, Bastan B, Fisher M, Bouley J, Henninger N.

Brain Res. 2009 Jul 7;1279:182-8. doi: 10.1016/j.brainres.2009.05.002. Epub 2009 May 8.

PMID:
19427841
7.

Diffusion-Weighted MRI Stroke Volume Following Recanalization Treatment is Threshold-Dependent.

Sah RG, d'Esterre CD, Hill MD, Hafeez M, Tariq S, Forkert ND, Demchuk AM, Goyal M, Barber PA.

Clin Neuroradiol. 2019 Mar;29(1):135-141. doi: 10.1007/s00062-017-0634-4. Epub 2017 Oct 19.

PMID:
29051996
8.

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
9.

Convolutional Neural Network-Based Automated Segmentation of the Spinal Cord and Contusion Injury: Deep Learning Biomarker Correlates of Motor Impairment in Acute Spinal Cord Injury.

McCoy DB, Dupont SM, Gros C, Cohen-Adad J, Huie RJ, Ferguson A, Duong-Fernandez X, Thomas LH, Singh V, Narvid J, Pascual L, Kyritsis N, Beattie MS, Bresnahan JC, Dhall S, Whetstone W, Talbott JF; TRACK-SCI Investigators.

AJNR Am J Neuroradiol. 2019 Apr;40(4):737-744. doi: 10.3174/ajnr.A6020. Epub 2019 Mar 28.

PMID:
30923086
10.

Fully automated segmentation of prostate whole gland and transition zone in diffusion-weighted MRI using convolutional neural networks.

Clark T, Zhang J, Baig S, Wong A, Haider MA, Khalvati F.

J Med Imaging (Bellingham). 2017 Oct;4(4):041307. doi: 10.1117/1.JMI.4.4.041307. Epub 2017 Oct 17.

11.

In Acute Stroke, Can CT Perfusion-Derived Cerebral Blood Volume Maps Substitute for Diffusion-Weighted Imaging in Identifying the Ischemic Core?

Copen WA, Morais LT, Wu O, Schwamm LH, Schaefer PW, González RG, Yoo AJ.

PLoS One. 2015 Jul 20;10(7):e0133566. doi: 10.1371/journal.pone.0133566. eCollection 2015.

12.

Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks.

Gros C, De Leener B, Badji A, Maranzano J, Eden D, Dupont SM, Talbott J, Zhuoquiong R, Liu Y, Granberg T, Ouellette R, Tachibana Y, Hori M, Kamiya K, Chougar L, Stawiarz L, Hillert J, Bannier E, Kerbrat A, Edan G, Labauge P, Callot V, Pelletier J, Audoin B, Rasoanandrianina H, Brisset JC, Valsasina P, Rocca MA, Filippi M, Bakshi R, Tauhid S, Prados F, Yiannakas M, Kearney H, Ciccarelli O, Smith S, Treaba CA, Mainero C, Lefeuvre J, Reich DS, Nair G, Auclair V, McLaren DG, Martin AR, Fehlings MG, Vahdat S, Khatibi A, Doyon J, Shepherd T, Charlson E, Narayanan S, Cohen-Adad J.

Neuroimage. 2019 Jan 1;184:901-915. doi: 10.1016/j.neuroimage.2018.09.081. Epub 2018 Oct 6.

PMID:
30300751
13.

Low-to-high b value DWI ratio approaches in multiparametric MRI of the prostate: feasibility, optimal combination of b values, and comparison with ADC maps for the visual presentation of prostate cancer.

Xi Y, Liu A, Olumba F, Lawson P, Costa DN, Yuan Q, Khatri G, Yokoo T, Pedrosa I, Lenkinski RE.

Quant Imaging Med Surg. 2018 Jul;8(6):557-567. doi: 10.21037/qims.2018.06.08.

14.

White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks.

Guerrero R, Qin C, Oktay O, Bowles C, Chen L, Joules R, Wolz R, Valdés-Hernández MC, Dickie DA, Wardlaw J, Rueckert D.

Neuroimage Clin. 2017 Dec 20;17:918-934. doi: 10.1016/j.nicl.2017.12.022. eCollection 2018.

15.

Lesion segmentation from multimodal MRI using random forest following ischemic stroke.

Mitra J, Bourgeat P, Fripp J, Ghose S, Rose S, Salvado O, Connelly A, Campbell B, Palmer S, Sharma G, Christensen S, Carey L.

Neuroimage. 2014 Sep;98:324-35. doi: 10.1016/j.neuroimage.2014.04.056. Epub 2014 May 2.

PMID:
24793830
16.

Co-trained convolutional neural networks for automated detection of prostate cancer in multi-parametric MRI.

Yang X, Liu C, Wang Z, Yang J, Min HL, Wang L, Cheng KT.

Med Image Anal. 2017 Dec;42:212-227. doi: 10.1016/j.media.2017.08.006. Epub 2017 Aug 24.

PMID:
28850876
17.

Automated and Semiautomated Segmentation of Rectal Tumor Volumes on Diffusion-Weighted MRI: Can It Replace Manual Volumetry?

van Heeswijk MM, Lambregts DM, van Griethuysen JJ, Oei S, Rao SX, de Graaff CA, Vliegen RF, Beets GL, Papanikolaou N, Beets-Tan RG.

Int J Radiat Oncol Biol Phys. 2016 Mar 15;94(4):824-31. doi: 10.1016/j.ijrobp.2015.12.017. Epub 2015 Dec 17.

PMID:
26972655
18.

Automated analysis of cardiovascular magnetic resonance myocardial native T1 mapping images using fully convolutional neural networks.

Fahmy AS, El-Rewaidy H, Nezafat M, Nakamori S, Nezafat R.

J Cardiovasc Magn Reson. 2019 Jan 14;21(1):7. doi: 10.1186/s12968-018-0516-1.

19.

Comparison of T2-Weighted Imaging, DWI, and Dynamic Contrast-Enhanced MRI for Calculation of Prostate Cancer Index Lesion Volume: Correlation With Whole-Mount Pathology.

Sun C, Chatterjee A, Yousuf A, Antic T, Eggener S, Karczmar GS, Oto A.

AJR Am J Roentgenol. 2019 Feb;212(2):351-356. doi: 10.2214/AJR.18.20147. Epub 2018 Dec 12.

PMID:
30540213
20.

Automated segmentation of the knee for age assessment in 3D MR images using convolutional neural networks.

Pröve PL, Jopp-van Well E, Stanczus B, Morlock MM, Herrmann J, Groth M, Säring D, Auf der Mauer M.

Int J Legal Med. 2019 Jul;133(4):1191-1205. doi: 10.1007/s00414-018-1953-y. Epub 2018 Nov 3.

PMID:
30392059

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