114 related articles for article (PubMed ID: 38922790)
1. Radiomics as a measure superior to common similarity metrics for tumor segmentation performance evaluation.
Akramova R; Watanabe Y
J Appl Clin Med Phys; 2024 Jun; ():e14442. PubMed ID: 38922790
[TBL] [Abstract][Full Text] [Related]
2. Multiple U-Net-Based Automatic Segmentations and Radiomics Feature Stability on Ultrasound Images for Patients With Ovarian Cancer.
Jin J; Zhu H; Zhang J; Ai Y; Zhang J; Teng Y; Xie C; Jin X
Front Oncol; 2020; 10():614201. PubMed ID: 33680934
[TBL] [Abstract][Full Text] [Related]
3. CNN-based automatic segmentations and radiomics feature reliability on contrast-enhanced ultrasound images for renal tumors.
Yang Y; Chen F; Liang H; Bai Y; Wang Z; Zhao L; Ma S; Niu Q; Li F; Xie T; Cai Y
Front Oncol; 2023; 13():1166988. PubMed ID: 37333811
[TBL] [Abstract][Full Text] [Related]
4. Fully automated segmentation and radiomics feature extraction of hypopharyngeal cancer on MRI using deep learning.
Lin YC; Lin G; Pandey S; Yeh CH; Wang JJ; Lin CY; Ho TY; Ko SF; Ng SH
Eur Radiol; 2023 Sep; 33(9):6548-6556. PubMed ID: 37338554
[TBL] [Abstract][Full Text] [Related]
5. A Critical Analysis of the Robustness of Radiomics to Variations in Segmentation Methods in
Pasini G; Russo G; Mantarro C; Bini F; Richiusa S; Morgante L; Comelli A; Russo GI; Sabini MG; Cosentino S; Marinozzi F; Ippolito M; Stefano A
Diagnostics (Basel); 2023 Dec; 13(24):. PubMed ID: 38132224
[TBL] [Abstract][Full Text] [Related]
6. Deep learning-based automatic segmentation of meningioma from T1-weighted contrast-enhanced MRI for preoperative meningioma differentiation using radiomic features.
Yang L; Wang T; Zhang J; Kang S; Xu S; Wang K
BMC Med Imaging; 2024 Mar; 24(1):56. PubMed ID: 38443817
[TBL] [Abstract][Full Text] [Related]
7. Automated multiclass segmentation, quantification, and visualization of the diseased aorta on hybrid PET/CT-SEQUOIA.
van Praagh GD; Nienhuis PH; Reijrink M; Davidse MEJ; Duff LM; Spottiswoode BS; Mulder DJ; Prakken NHJ; Scarsbrook AF; Morgan AW; Tsoumpas C; Wolterink JM; Mouridsen KB; Borra RJH; Sinha B; Slart RHJA
Med Phys; 2024 Jun; 51(6):4297-4310. PubMed ID: 38323867
[TBL] [Abstract][Full Text] [Related]
8. Comprehensive benchmarking of CNN-based tumor segmentation methods using multimodal MRI data.
Kundal K; Rao KV; Majumdar A; Kumar N; Kumar R
Comput Biol Med; 2024 Jun; 178():108799. PubMed ID: 38925087
[TBL] [Abstract][Full Text] [Related]
9. Lung tumor segmentation methods: Impact on the uncertainty of radiomics features for non-small cell lung cancer.
Owens CA; Peterson CB; Tang C; Koay EJ; Yu W; Mackin DS; Li J; Salehpour MR; Fuentes DT; Court LE; Yang J
PLoS One; 2018; 13(10):e0205003. PubMed ID: 30286184
[TBL] [Abstract][Full Text] [Related]
10. Validity and reliability of masseter muscles segmentation from the transverse sections of Cone-Beam CT scans compared with MRI scans.
Pan Y; Wang Y; Li G; Chen S; Xu T
Int J Comput Assist Radiol Surg; 2022 Apr; 17(4):751-759. PubMed ID: 34625872
[TBL] [Abstract][Full Text] [Related]
11. Deep cross-modality (MR-CT) educed distillation learning for cone beam CT lung tumor segmentation.
Jiang J; Riyahi Alam S; Chen I; Zhang P; Rimner A; Deasy JO; Veeraraghavan H
Med Phys; 2021 Jul; 48(7):3702-3713. PubMed ID: 33905558
[TBL] [Abstract][Full Text] [Related]
12. Evaluation of deep learning-based multiparametric MRI oropharyngeal primary tumor auto-segmentation and investigation of input channel effects: Results from a prospective imaging registry.
Wahid KA; Ahmed S; He R; van Dijk LV; Teuwen J; McDonald BA; Salama V; Mohamed ASR; Salzillo T; Dede C; Taku N; Lai SY; Fuller CD; Naser MA
Clin Transl Radiat Oncol; 2022 Jan; 32():6-14. PubMed ID: 34765748
[TBL] [Abstract][Full Text] [Related]
13. The Accuracy and Radiomics Feature Effects of Multiple U-net-Based Automatic Segmentation Models for Transvaginal Ultrasound Images of Cervical Cancer.
Jin J; Zhu H; Teng Y; Ai Y; Xie C; Jin X
J Digit Imaging; 2022 Aug; 35(4):983-992. PubMed ID: 35355160
[TBL] [Abstract][Full Text] [Related]
14. Effects of interobserver and interdisciplinary segmentation variabilities on CT-based radiomics for pancreatic cancer.
Wong J; Baine M; Wisnoskie S; Bennion N; Zheng D; Yu L; Dalal V; Hollingsworth MA; Lin C; Zheng D
Sci Rep; 2021 Aug; 11(1):16328. PubMed ID: 34381070
[TBL] [Abstract][Full Text] [Related]
15. CT-based deep learning segmentation of ovarian cancer and the stability of the extracted radiomics features.
Wang Y; Wang M; Cao P; Wong EMF; Ho G; Lam TPW; Han L; Lee EYP
Quant Imaging Med Surg; 2023 Aug; 13(8):5218-5229. PubMed ID: 37581064
[TBL] [Abstract][Full Text] [Related]
16. MRI-based radiomics in breast cancer: feature robustness with respect to inter-observer segmentation variability.
Granzier RWY; Verbakel NMH; Ibrahim A; van Timmeren JE; van Nijnatten TJA; Leijenaar RTH; Lobbes MBI; Smidt ML; Woodruff HC
Sci Rep; 2020 Aug; 10(1):14163. PubMed ID: 32843663
[TBL] [Abstract][Full Text] [Related]
17. Application of simultaneous uncertainty quantification for image segmentation with probabilistic deep learning: Performance benchmarking of oropharyngeal cancer target delineation as a use-case.
Sahlsten J; Jaskari J; Wahid KA; Ahmed S; Glerean E; He R; Kann BH; Mäkitie A; Fuller CD; Naser MA; Kaski K
medRxiv; 2023 Feb; ():. PubMed ID: 36865296
[TBL] [Abstract][Full Text] [Related]
18. Reproducibility of F18-FDG PET radiomic features for different cervical tumor segmentation methods, gray-level discretization, and reconstruction algorithms.
Altazi BA; Zhang GG; Fernandez DC; Montejo ME; Hunt D; Werner J; Biagioli MC; Moros EG
J Appl Clin Med Phys; 2017 Nov; 18(6):32-48. PubMed ID: 28891217
[TBL] [Abstract][Full Text] [Related]
19. 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. PubMed ID: 28756622
[TBL] [Abstract][Full Text] [Related]
20. Clinical implementation of artificial intelligence in neuroradiology with development of a novel workflow-efficient picture archiving and communication system-based automated brain tumor segmentation and radiomic feature extraction.
Aboian M; Bousabarah K; Kazarian E; Zeevi T; Holler W; Merkaj S; Cassinelli Petersen G; Bahar R; Subramanian H; Sunku P; Schrickel E; Bhawnani J; Zawalich M; Mahajan A; Malhotra A; Payabvash S; Tocino I; Lin M; Westerhoff M
Front Neurosci; 2022; 16():860208. PubMed ID: 36312024
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
