Kong D - Search Results

1

A region-appearance-based adaptive variational model for 3D liver segmentation.

Peng J, Dong F, Chen Y, Kong D. Peng J, et al. Among authors: kong d. Med Phys. 2014 Apr;41(4):043502. doi: 10.1118/1.4866837. Med Phys. 2014. PMID: 24694159

2

3D liver segmentation using multiple region appearances and graph cuts.

Peng J, Hu P, Lu F, Peng Z, Kong D, Zhang H. Peng J, et al. Among authors: kong d. Med Phys. 2015 Dec;42(12):6840-52. doi: 10.1118/1.4934834. Med Phys. 2015. PMID: 26632041

3

Automatic 3D liver location and segmentation via convolutional neural network and graph cut.

Lu F, Wu F, Hu P, Peng Z, Kong D. Lu F, et al. Among authors: kong d. Int J Comput Assist Radiol Surg. 2017 Feb;12(2):171-182. doi: 10.1007/s11548-016-1467-3. Epub 2016 Sep 7. Int J Comput Assist Radiol Surg. 2017. PMID: 27604760

4

Automatic 3D liver segmentation based on deep learning and globally optimized surface evolution.

Hu P, Wu F, Peng J, Liang P, Kong D. Hu P, et al. Among authors: kong d. Phys Med Biol. 2016 Dec 21;61(24):8676-8698. doi: 10.1088/1361-6560/61/24/8676. Epub 2016 Nov 23. Phys Med Biol. 2016. PMID: 27880735

5

Automatic abdominal multi-organ segmentation using deep convolutional neural network and time-implicit level sets.

Hu P, Wu F, Peng J, Bao Y, Chen F, Kong D. Hu P, et al. Among authors: kong d. Int J Comput Assist Radiol Surg. 2017 Mar;12(3):399-411. doi: 10.1007/s11548-016-1501-5. Epub 2016 Nov 24. Int J Comput Assist Radiol Surg. 2017. PMID: 27885540

6

Cascade convolutional neural networks for automatic detection of thyroid nodules in ultrasound images.

Ma J, Wu F, Jiang T, Zhu J, Kong D. Ma J, et al. Among authors: kong d. Med Phys. 2017 May;44(5):1678-1691. doi: 10.1002/mp.12134. Epub 2017 Apr 17. Med Phys. 2017. PMID: 28186630

7

Pulmonary nodule detection using hybrid two-stage 3D CNNs.

Tan M, Wu F, Yang B, Ma J, Kong D, Chen Z, Long D. Tan M, et al. Among authors: kong d. Med Phys. 2020 Aug;47(8):3376-3388. doi: 10.1002/mp.14161. Epub 2020 Jul 6. Med Phys. 2020. PMID: 32239521

8

Automatic liver segmentation using 3D convolutional neural networks with a hybrid loss function.

Tan M, Wu F, Kong D, Mao X. Tan M, et al. Among authors: kong d. Med Phys. 2021 Apr;48(4):1707-1719. doi: 10.1002/mp.14732. Epub 2021 Mar 4. Med Phys. 2021. PMID: 33496971

9

A Semiautomated Deep Learning Approach for Pancreas Segmentation.

Huang M, Huang C, Yuan J, Kong D. Huang M, et al. Among authors: kong d. J Healthc Eng. 2021 Jul 2;2021:3284493. doi: 10.1155/2021/3284493. eCollection 2021. J Healthc Eng. 2021. PMID: 34306587 Free PMC article.

10

EPolar-UNet: An edge-attending polar UNet for automatic medical image segmentation with small datasets.

Ling Y, Wang Y, Liu Q, Yu J, Xu L, Zhang X, Liang P, Kong D. Ling Y, et al. Among authors: kong d. Med Phys. 2024 Mar;51(3):1702-1713. doi: 10.1002/mp.16957. Epub 2024 Feb 1. Med Phys. 2024. PMID: 38299370

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