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Items: 1 to 20 of 163

1.

Fully Automatic Segmentation of Acute Ischemic Lesions on Diffusion-Weighted Imaging Using Convolutional Neural Networks: Comparison with Conventional Algorithms.

Woo I, Lee A, Jung SC, Lee H, Kim N, Cho SJ, Kim D, Lee J, Sunwoo L, Kang DW.

Korean J Radiol. 2019 Aug;20(8):1275-1284. doi: 10.3348/kjr.2018.0615.

2.

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
3.

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.

4.

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.

5.

Automatic Segmentation of Acute Ischemic Stroke From DWI Using 3-D Fully Convolutional DenseNets.

Zhang R, Zhao L, Lou W, Abrigo JM, Mok VCT, Chu WCW, Wang D, Shi L.

IEEE Trans Med Imaging. 2018 Sep;37(9):2149-2160. doi: 10.1109/TMI.2018.2821244. Epub 2018 Mar 30.

PMID:
29994088
6.

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
7.

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.

8.

Automatic segmentation of cerebral infarcts in follow-up computed tomography images with convolutional neural networks.

Sales Barros R, Tolhuisen ML, Boers AM, Jansen I, Ponomareva E, Dippel DWJ, van der Lugt A, van Oostenbrugge RJ, van Zwam WH, Berkhemer OA, Goyal M, Demchuk AM, Menon BK, Mitchell P, Hill MD, Jovin TG, Davalos A, Campbell BCV, Saver JL, Roos YBWEM, Muir KW, White P, Bracard S, Guillemin F, Olabarriaga SD, Majoie CBLM, Marquering HA.

J Neurointerv Surg. 2019 Dec 23. pii: neurintsurg-2019-015471. doi: 10.1136/neurintsurg-2019-015471. [Epub ahead of print]

PMID:
31871069
9.

Attention-enabled 3D boosted convolutional neural networks for semantic CT segmentation using deep supervision.

Kearney V, Chan JW, Wang T, Perry A, Yom SS, Solberg TD.

Phys Med Biol. 2019 Jul 2;64(13):135001. doi: 10.1088/1361-6560/ab2818.

PMID:
31181561
10.

Deep learning for fully automated tumor segmentation and extraction of magnetic resonance radiomics features in cervical cancer.

Lin YC, Lin CH, Lu HY, Chiang HJ, Wang HK, Huang YT, Ng SH, Hong JH, Yen TC, Lai CH, Lin G.

Eur Radiol. 2019 Nov 11. doi: 10.1007/s00330-019-06467-3. [Epub ahead of print]

PMID:
31712961
11.

Quantification of tumor burden in multiple myeloma by atlas-based semi-automatic segmentation of WB-DWI.

Almeida SD, Santinha J, Oliveira FPM, Ip J, Lisitskaya M, Lourenço J, Uysal A, Matos C, João C, Papanikolaou N.

Cancer Imaging. 2020 Jan 13;20(1):6. doi: 10.1186/s40644-020-0286-5.

12.

The role of quantitative neuroimaging indices in the differentiation of ischemia from demyelination: an analytical study with case presentation.

Hoque R, Ledbetter C, Gonzalez-Toledo E, Misra V, Menon U, Kenner M, Rabinstein AA, Kelley RE, Zivadinov R, Minagar A.

Int Rev Neurobiol. 2007;79:491-519.

PMID:
17531856
13.

Automatic lesion detection and segmentation of 18F-FET PET in gliomas: A full 3D U-Net convolutional neural network study.

Blanc-Durand P, Van Der Gucht A, Schaefer N, Itti E, Prior JO.

PLoS One. 2018 Apr 13;13(4):e0195798. doi: 10.1371/journal.pone.0195798. eCollection 2018.

14.

Automatic Tumor Segmentation With a Convolutional Neural Network in Multiparametric MRI: Influence of Distortion Correction.

Bielak L, Wiedenmann N, Nicolay NH, Lottner T, Fischer J, Bunea H, Grosu AL, Bock M.

Tomography. 2019 Sep;5(3):292-299. doi: 10.18383/j.tom.2019.00010.

15.

An artificial intelligence framework for automatic segmentation and volumetry of vestibular schwannomas from contrast-enhanced T1-weighted and high-resolution T2-weighted MRI.

Shapey J, Wang G, Dorent R, Dimitriadis A, Li W, Paddick I, Kitchen N, Bisdas S, Saeed SR, Ourselin S, Bradford R, Vercauteren T.

J Neurosurg. 2019 Dec 6:1-9. doi: 10.3171/2019.9.JNS191949. [Epub ahead of print]

PMID:
31812137
16.

Nested Dilation Networks for Brain Tumor Segmentation Based on Magnetic Resonance Imaging.

Wang L, Wang S, Chen R, Qu X, Chen Y, Huang S, Liu C.

Front Neurosci. 2019 Apr 5;13:285. doi: 10.3389/fnins.2019.00285. eCollection 2019.

17.

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.

18.

Segmentation of the Proximal Femur from MR Images using Deep Convolutional Neural Networks.

Deniz CM, Xiang S, Hallyburton RS, Welbeck A, Babb JS, Honig S, Cho K, Chang G.

Sci Rep. 2018 Nov 7;8(1):16485. doi: 10.1038/s41598-018-34817-6.

19.

Active learning strategy and hybrid training for infarct segmentation on diffusion MRI with a U-shaped network.

Olivier A, Moal O, Moal B, Munsch F, Okubo G, Sibon I, Dousset V, Tourdias T.

J Med Imaging (Bellingham). 2019 Oct;6(4):044001. doi: 10.1117/1.JMI.6.4.044001. Epub 2019 Oct 4.

PMID:
31592439
20.

Superpixel-based and boundary-sensitive convolutional neural network for automated liver segmentation.

Qin W, Wu J, Han F, Yuan Y, Zhao W, Ibragimov B, Gu J, Xing L.

Phys Med Biol. 2018 May 4;63(9):095017. doi: 10.1088/1361-6560/aabd19.

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