107 related articles for article (PubMed ID: 37859296)
1. Segmentation of kidney mass using AgDenseU-Net 2.5D model.
Sun P; Mo Z; Hu F; Song X; Mo T; Yu B; Zhang Y; Chen Z
Comput Biol Med; 2022 Nov; 150():106223. PubMed ID: 37859296
[TBL] [Abstract][Full Text] [Related]
2. 2.5D MFFAU-Net: a convolutional neural network for kidney segmentation.
Sun P; Mo Z; Hu F; Song X; Mo T; Yu B; Zhang Y; Chen Z
BMC Med Inform Decis Mak; 2023 May; 23(1):92. PubMed ID: 37165349
[TBL] [Abstract][Full Text] [Related]
3. MTAN: A semi-supervised learning model for kidney tumor segmentation.
Sun P; Yang S; Guan H; Mo T; Yu B; Chen Z
J Xray Sci Technol; 2023; 31(6):1295-1313. PubMed ID: 37718833
[TBL] [Abstract][Full Text] [Related]
4. Automated segmentation of kidney and renal mass and automated detection of renal mass in CT urography using 3D U-Net-based deep convolutional neural network.
Lin Z; Cui Y; Liu J; Sun Z; Ma S; Zhang X; Wang X
Eur Radiol; 2021 Jul; 31(7):5021-5031. PubMed ID: 33439313
[TBL] [Abstract][Full Text] [Related]
5. FYU-Net: A Cascading Segmentation Network for Kidney Tumor Medical Imaging.
Feng H; Kou X; Tang Z; Li L
Comput Math Methods Med; 2022; 2022():4792532. PubMed ID: 36303948
[TBL] [Abstract][Full Text] [Related]
6. The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 challenge.
Heller N; Isensee F; Maier-Hein KH; Hou X; Xie C; Li F; Nan Y; Mu G; Lin Z; Han M; Yao G; Gao Y; Zhang Y; Wang Y; Hou F; Yang J; Xiong G; Tian J; Zhong C; Ma J; Rickman J; Dean J; Stai B; Tejpaul R; Oestreich M; Blake P; Kaluzniak H; Raza S; Rosenberg J; Moore K; Walczak E; Rengel Z; Edgerton Z; Vasdev R; Peterson M; McSweeney S; Peterson S; Kalapara A; Sathianathen N; Papanikolopoulos N; Weight C
Med Image Anal; 2021 Jan; 67():101821. PubMed ID: 33049579
[TBL] [Abstract][Full Text] [Related]
7. Automatic kidney segmentation using 2.5D ResUNet and 2.5D DenseUNet for malignant potential analysis in complex renal cyst based on CT images.
Kittipongdaja P; Siriborvornratanakul T
EURASIP J Image Video Process; 2022; 2022(1):5. PubMed ID: 35340560
[TBL] [Abstract][Full Text] [Related]
8. Boundary Attention U-Net for Kidney and Kidney Tumor Segmentation.
Zhao Z; Chen H; Li J; Wang L
Annu Int Conf IEEE Eng Med Biol Soc; 2022 Jul; 2022():1540-1543. PubMed ID: 36085739
[TBL] [Abstract][Full Text] [Related]
9. Automatic renal mass segmentation and classification on CT images based on 3D U-Net and ResNet algorithms.
Zhao T; Sun Z; Guo Y; Sun Y; Zhang Y; Wang X
Front Oncol; 2023; 13():1169922. PubMed ID: 37274226
[TBL] [Abstract][Full Text] [Related]
10. An Ensemble of U-Net Models for Kidney Tumor Segmentation With CT Images.
Causey J; Stubblefield J; Qualls J; Fowler J; Cai L; Walker K; Guan Y; Huang X
IEEE/ACM Trans Comput Biol Bioinform; 2022; 19(3):1387-1392. PubMed ID: 34061750
[TBL] [Abstract][Full Text] [Related]
11. Automatic cyst and kidney segmentation in autosomal dominant polycystic kidney disease: Comparison of U-Net based methods.
Rombolotti M; Sangalli F; Cerullo D; Remuzzi A; Lanzarone E
Comput Biol Med; 2022 Jul; 146():105431. PubMed ID: 35751190
[TBL] [Abstract][Full Text] [Related]
12. Kidney segmentation from computed tomography images using deep neural network.
da Cruz LB; Araújo JDL; Ferreira JL; Diniz JOB; Silva AC; de Almeida JDS; de Paiva AC; Gattass M
Comput Biol Med; 2020 Aug; 123():103906. PubMed ID: 32768047
[TBL] [Abstract][Full Text] [Related]
13. Effect of Dataset Size and Medical Image Modality on Convolutional Neural Network Model Performance for Automated Segmentation: A CT and MR Renal Tumor Imaging Study.
Gottlich HC; Gregory AV; Sharma V; Khanna A; Moustafa AU; Lohse CM; Potretzke TA; Korfiatis P; Potretzke AM; Denic A; Rule AD; Takahashi N; Erickson BJ; Leibovich BC; Kline TL
J Digit Imaging; 2023 Aug; 36(4):1770-1781. PubMed ID: 36932251
[TBL] [Abstract][Full Text] [Related]
14. A novel adaptive cubic quasi-Newton optimizer for deep learning based medical image analysis tasks, validated on detection of COVID-19 and segmentation for COVID-19 lung infection, liver tumor, and optic disc/cup.
Liu Y; Zhang M; Zhong Z; Zeng X
Med Phys; 2023 Mar; 50(3):1528-1538. PubMed ID: 36057788
[TBL] [Abstract][Full Text] [Related]
15. A Deep Learning Approach for Automated Segmentation of Kidneys and Exophytic Cysts in Individuals with Autosomal Dominant Polycystic Kidney Disease.
Kim Y; Tao C; Kim H; Oh GY; Ko J; Bae KT
J Am Soc Nephrol; 2022 Aug; 33(8):1581-1589. PubMed ID: 35768178
[TBL] [Abstract][Full Text] [Related]
16. A lightweight hierarchical convolution network for brain tumor segmentation.
Wang Y; Cao Y; Li J; Wu H; Wang S; Dong X; Yu H
BMC Bioinformatics; 2022 Dec; 22(Suppl 5):636. PubMed ID: 36513986
[TBL] [Abstract][Full Text] [Related]
17. Kidney Tumor Segmentation Based on FR2PAttU-Net Model.
Sun P; Mo Z; Hu F; Liu F; Mo T; Zhang Y; Chen Z
Front Oncol; 2022; 12():853281. PubMed ID: 35372025
[TBL] [Abstract][Full Text] [Related]
18. Ensemble U-net-based method for fully automated detection and segmentation of renal masses on computed tomography images.
Fatemeh Z; Nicola S; Satheesh K; Eranga U
Med Phys; 2020 Sep; 47(9):4032-4044. PubMed ID: 32329074
[TBL] [Abstract][Full Text] [Related]
19. Active learning for accuracy enhancement of semantic segmentation with CNN-corrected label curations: Evaluation on kidney segmentation in abdominal CT.
Kim T; Lee KH; Ham S; Park B; Lee S; Hong D; Kim GB; Kyung YS; Kim CS; Kim N
Sci Rep; 2020 Jan; 10(1):366. PubMed ID: 31941938
[TBL] [Abstract][Full Text] [Related]
20. MBD-Net: Multi-Branch Dilated Convolutional Network With Cyst Discriminator for Renal Multi-Structure Segmentation.
Liu Y; Zhao Y; Wang M; Hao Y; Wang X; Wang L
Annu Int Conf IEEE Eng Med Biol Soc; 2023 Jul; 2023():1-4. PubMed ID: 38082702
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
