489 related articles for article (PubMed ID: 33461500)
1. MIScnn: a framework for medical image segmentation with convolutional neural networks and deep learning.
Müller D; Kramer F
BMC Med Imaging; 2021 Jan; 21(1):12. PubMed ID: 33461500
[TBL] [Abstract][Full Text] [Related]
2. TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning.
Pérez-García F; Sparks R; Ourselin S
Comput Methods Programs Biomed; 2021 Sep; 208():106236. PubMed ID: 34311413
[TBL] [Abstract][Full Text] [Related]
3. DeepImageTranslator: A free, user-friendly graphical interface for image translation using deep-learning and its applications in 3D CT image analysis.
Ye RZ; Noll C; Richard G; Lepage M; Turcotte ÉE; Carpentier AC
SLAS Technol; 2022 Feb; 27(1):76-84. PubMed ID: 35058205
[TBL] [Abstract][Full Text] [Related]
4. MISeval: A Metric Library for Medical Image Segmentation Evaluation.
Müller D; Hartmann D; Meyer P; Auer F; Soto-Rey I; Kramer F
Stud Health Technol Inform; 2022 May; 294():33-37. PubMed ID: 35612011
[TBL] [Abstract][Full Text] [Related]
5. Efficient deep learning architecture with dimension-wise pyramid pooling for nuclei segmentation of histopathology images.
Aatresh AA; Yatgiri RP; Chanchal AK; Kumar A; Ravi A; Das D; Bs R; Lal S; Kini J
Comput Med Imaging Graph; 2021 Oct; 93():101975. PubMed ID: 34461375
[TBL] [Abstract][Full Text] [Related]
6. Bi-channel image registration and deep-learning segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain.
Wang X; Zeng W; Yang X; Zhang Y; Fang C; Zeng S; Han Y; Fei P
Elife; 2021 Jan; 10():. PubMed ID: 33459255
[TBL] [Abstract][Full Text] [Related]
7. Detection, segmentation, and 3D pose estimation of surgical tools using convolutional neural networks and algebraic geometry.
Hasan MK; Calvet L; Rabbani N; Bartoli A
Med Image Anal; 2021 May; 70():101994. PubMed ID: 33611053
[TBL] [Abstract][Full Text] [Related]
8. PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation.
Wang G; Luo X; Gu R; Yang S; Qu Y; Zhai S; Zhao Q; Li K; Zhang S
Comput Methods Programs Biomed; 2023 Apr; 231():107398. PubMed ID: 36773591
[TBL] [Abstract][Full Text] [Related]
9. PyRaDiSe: A Python package for DICOM-RT-based auto-segmentation pipeline construction and DICOM-RT data conversion.
Rüfenacht E; Kamath A; Suter Y; Poel R; Ermiş E; Scheib S; Reyes M
Comput Methods Programs Biomed; 2023 Apr; 231():107374. PubMed ID: 36738608
[TBL] [Abstract][Full Text] [Related]
10. Semi-supervised learning for automatic segmentation of the knee from MRI with convolutional neural networks.
Burton W; Myers C; Rullkoetter P
Comput Methods Programs Biomed; 2020 Jun; 189():105328. PubMed ID: 31958580
[TBL] [Abstract][Full Text] [Related]
11. A deep learning algorithm for one-step contour aware nuclei segmentation of histopathology images.
Cui Y; Zhang G; Liu Z; Xiong Z; Hu J
Med Biol Eng Comput; 2019 Sep; 57(9):2027-2043. PubMed ID: 31346949
[TBL] [Abstract][Full Text] [Related]
12. A high-performance deep-learning-based pipeline for whole-brain vasculature segmentation at the capillary resolution.
Li Y; Liu X; Jia X; Jiang T; Wu J; Zhang Q; Li J; Li X; Li A
Bioinformatics; 2023 Apr; 39(4):. PubMed ID: 36946294
[TBL] [Abstract][Full Text] [Related]
13. DENSE-INception U-net for medical image segmentation.
Zhang Z; Wu C; Coleman S; Kerr D
Comput Methods Programs Biomed; 2020 Aug; 192():105395. PubMed ID: 32163817
[TBL] [Abstract][Full Text] [Related]
14. Collaborative networks of transformers and convolutional neural networks are powerful and versatile learners for accurate 3D medical image segmentation.
Chen Y; Lu X; Xie Q
Comput Biol Med; 2023 Sep; 164():107228. PubMed ID: 37473563
[TBL] [Abstract][Full Text] [Related]
15. Fully automatic tumor segmentation of breast ultrasound images with deep learning.
Zhang S; Liao M; Wang J; Zhu Y; Zhang Y; Zhang J; Zheng R; Lv L; Zhu D; Chen H; Wang W
J Appl Clin Med Phys; 2023 Jan; 24(1):e13863. PubMed ID: 36495018
[TBL] [Abstract][Full Text] [Related]
16. U-Net based deep learning bladder segmentation in CT urography.
Ma X; Hadjiiski LM; Wei J; Chan HP; Cha KH; Cohan RH; Caoili EM; Samala R; Zhou C; Lu Y
Med Phys; 2019 Apr; 46(4):1752-1765. PubMed ID: 30734932
[TBL] [Abstract][Full Text] [Related]
17. InstantDL: an easy-to-use deep learning pipeline for image segmentation and classification.
Waibel DJE; Shetab Boushehri S; Marr C
BMC Bioinformatics; 2021 Mar; 22(1):103. PubMed ID: 33653266
[TBL] [Abstract][Full Text] [Related]
18. Multi-Scale deep learning framework for cochlea localization, segmentation and analysis on clinical ultra-high-resolution CT images.
Heutink F; Koch V; Verbist B; van der Woude WJ; Mylanus E; Huinck W; Sechopoulos I; Caballo M
Comput Methods Programs Biomed; 2020 Jul; 191():105387. PubMed ID: 32109685
[TBL] [Abstract][Full Text] [Related]
19. pymia: A Python package for data handling and evaluation in deep learning-based medical image analysis.
Jungo A; Scheidegger O; Reyes M; Balsiger F
Comput Methods Programs Biomed; 2021 Jan; 198():105796. PubMed ID: 33137700
[TBL] [Abstract][Full Text] [Related]
20. Robust deep learning-based semantic organ segmentation in hyperspectral images.
Seidlitz S; Sellner J; Odenthal J; Özdemir B; Studier-Fischer A; Knödler S; Ayala L; Adler TJ; Kenngott HG; Tizabi M; Wagner M; Nickel F; Müller-Stich BP; Maier-Hein L
Med Image Anal; 2022 Aug; 80():102488. PubMed ID: 35667327
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]