117 related articles for article (PubMed ID: 37540449)
1. Graph-based automatic detection and classification of lesion changes in pairs of CT studies for oncology follow-up.
Rochman S; Szeskin A; Lederman R; Sosna J; Joskowicz L
Int J Comput Assist Radiol Surg; 2024 Feb; 19(2):241-251. PubMed ID: 37540449
[TBL] [Abstract][Full Text] [Related]
2. Liver lesion changes analysis in longitudinal CECT scans by simultaneous deep learning voxel classification with SimU-Net.
Szeskin A; Rochman S; Weiss S; Lederman R; Sosna J; Joskowicz L
Med Image Anal; 2023 Jan; 83():102675. PubMed ID: 36334393
[TBL] [Abstract][Full Text] [Related]
3. Automatic detection of new tumors and tumor burden evaluation in longitudinal liver CT scan studies.
Vivanti R; Szeskin A; Lev-Cohain N; Sosna J; Joskowicz L
Int J Comput Assist Radiol Surg; 2017 Nov; 12(11):1945-1957. PubMed ID: 28856515
[TBL] [Abstract][Full Text] [Related]
4. Follow-up of liver metastases: a comparison of deep learning and RECIST 1.1.
Joskowicz L; Szeskin A; Rochman S; Dodi A; Lederman R; Fruchtman-Brot H; Azraq Y; Sosna J
Eur Radiol; 2023 Dec; 33(12):9320-9327. PubMed ID: 37480549
[TBL] [Abstract][Full Text] [Related]
5. Automatic lung tumor segmentation with leaks removal in follow-up CT studies.
Vivanti R; Joskowicz L; Karaaslan OA; Sosna J
Int J Comput Assist Radiol Surg; 2015 Sep; 10(9):1505-14. PubMed ID: 25605297
[TBL] [Abstract][Full Text] [Related]
6. Validation of lesion simulations in clinical CT data for anonymized chest and abdominal CT databases.
Robins M; Solomon J; Koweek LMH; Christensen J; Samei E
Med Phys; 2019 Apr; 46(4):1931-1937. PubMed ID: 30703259
[TBL] [Abstract][Full Text] [Related]
7. A new fully automatic and robust algorithm for fast segmentation of liver tissue and tumors from CT scans.
Massoptier L; Casciaro S
Eur Radiol; 2008 Aug; 18(8):1658-65. PubMed ID: 18369633
[TBL] [Abstract][Full Text] [Related]
8. Liver lesion localisation and classification with convolutional neural networks: a comparison between conventional and spectral computed tomography.
Shapira N; Fokuhl J; Schultheiß M; Beck S; Kopp FK; Pfeiffer D; Dangelmaier J; Pahn G; Sauter AP; Renger B; Fingerle AA; Rummeny EJ; Albarqouni S; Navab N; Noël PB
Biomed Phys Eng Express; 2020 Jan; 6(1):015038. PubMed ID: 33438626
[TBL] [Abstract][Full Text] [Related]
9. A fully automatic end-to-end method for content-based image retrieval of CT scans with similar liver lesion annotations.
Spanier AB; Caplan N; Sosna J; Acar B; Joskowicz L
Int J Comput Assist Radiol Surg; 2018 Jan; 13(1):165-174. PubMed ID: 29147954
[TBL] [Abstract][Full Text] [Related]
10. Patient-specific and global convolutional neural networks for robust automatic liver tumor delineation in follow-up CT studies.
Vivanti R; Joskowicz L; Lev-Cohain N; Ephrat A; Sosna J
Med Biol Eng Comput; 2018 Sep; 56(9):1699-1713. PubMed ID: 29524116
[TBL] [Abstract][Full Text] [Related]
11. Automatic segmentation variability estimation with segmentation priors.
Joskowicz L; Cohen D; Caplan N; Sosna J
Med Image Anal; 2018 Dec; 50():54-64. PubMed ID: 30208356
[TBL] [Abstract][Full Text] [Related]
12. Performance of an automated registration-based method for longitudinal lesion matching and comparison to inter-reader variability.
Huff DT; Santoro-Fernandes V; Chen S; Chen M; Kashuk C; Weisman AJ; Jeraj R; Perk TG
Phys Med Biol; 2023 Aug; 68(17):. PubMed ID: 37567220
[No Abstract] [Full Text] [Related]
13. Shape-intensity prior level set combining probabilistic atlas and probability map constrains for automatic liver segmentation from abdominal CT images.
Wang J; Cheng Y; Guo C; Wang Y; Tamura S
Int J Comput Assist Radiol Surg; 2016 May; 11(5):817-26. PubMed ID: 26646416
[TBL] [Abstract][Full Text] [Related]
14. Automatic detection and classification of hypodense hepatic lesions on contrast-enhanced venous-phase CT.
Bilello M; Gokturk SB; Desser T; Napel S; Jeffrey RB; Beaulieu CF
Med Phys; 2004 Sep; 31(9):2584-93. PubMed ID: 15487741
[TBL] [Abstract][Full Text] [Related]
15. Quantifying the margin sharpness of lesions on radiological images for content-based image retrieval.
Xu J; Napel S; Greenspan H; Beaulieu CF; Agrawal N; Rubin D
Med Phys; 2012 Sep; 39(9):5405-18. PubMed ID: 22957608
[TBL] [Abstract][Full Text] [Related]
16. High resolution multidetector CT-aided tissue analysis and quantification of lung fibrosis.
Zavaletta VA; Bartholmai BJ; Robb RA
Acad Radiol; 2007 Jul; 14(7):772-87. PubMed ID: 17574128
[TBL] [Abstract][Full Text] [Related]
17. A computational pipeline for quantification of pulmonary infections in small animal models using serial PET-CT imaging.
Bagci U; Foster B; Miller-Jaster K; Luna B; Dey B; Bishai WR; Jonsson CB; Jain S; Mollura DJ
EJNMMI Res; 2013 Jul; 3(1):55. PubMed ID: 23879987
[TBL] [Abstract][Full Text] [Related]
18. Low-dose CT for the detection and classification of metastatic liver lesions: Results of the 2016 Low Dose CT Grand Challenge.
McCollough CH; Bartley AC; Carter RE; Chen B; Drees TA; Edwards P; Holmes DR; Huang AE; Khan F; Leng S; McMillan KL; Michalak GJ; Nunez KM; Yu L; Fletcher JG
Med Phys; 2017 Oct; 44(10):e339-e352. PubMed ID: 29027235
[TBL] [Abstract][Full Text] [Related]
19. A fully automatic artificial intelligence-based CT image analysis system for accurate detection, diagnosis, and quantitative severity evaluation of pulmonary tuberculosis.
Yan C; Wang L; Lin J; Xu J; Zhang T; Qi J; Li X; Ni W; Wu G; Huang J; Xu Y; Woodruff HC; Lambin P
Eur Radiol; 2022 Apr; 32(4):2188-2199. PubMed ID: 34842959
[TBL] [Abstract][Full Text] [Related]
20. Detection of vessel bifurcations in CT scans for automatic objective assessment of deformable image registration accuracy.
Cazoulat G; Anderson BM; McCulloch MM; Rigaud B; Koay EJ; Brock KK
Med Phys; 2021 Oct; 48(10):5935-5946. PubMed ID: 34390007
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]