113 related articles for article (PubMed ID: 37638845)
1. CORR Insights®: Development and Validation of a Convolutional Neural Network Model to Predict a Pathologic Fracture in the Proximal Femur Using Abdomen and Pelvis CT Images of Patients With Advanced Cancer.
Groot OQ
Clin Orthop Relat Res; 2023 Nov; 481(11):2257-2259. PubMed ID: 37638845
[No Abstract] [Full Text] [Related]
2. Development and Validation of a Convolutional Neural Network Model to Predict a Pathologic Fracture in the Proximal Femur Using Abdomen and Pelvis CT Images of Patients With Advanced Cancer.
Joo MW; Ko T; Kim MS; Lee YS; Shin SH; Chung YG; Lee HK
Clin Orthop Relat Res; 2023 Nov; 481(11):2247-2256. PubMed ID: 37615504
[TBL] [Abstract][Full Text] [Related]
3. Utility of a novel integrated deep convolutional neural network for the segmentation of hip joint from computed tomography images in the preoperative planning of total hip arthroplasty.
Wu D; Zhi X; Liu X; Zhang Y; Chai W
J Orthop Surg Res; 2022 Mar; 17(1):164. PubMed ID: 35292056
[TBL] [Abstract][Full Text] [Related]
4. Pediatric chest-abdomen-pelvis and abdomen-pelvis CT images with expert organ contours.
Jordan P; Adamson PM; Bhattbhatt V; Beriwal S; Shen S; Radermecker O; Bose S; Strain LS; Offe M; Fraley D; Principi S; Ye DH; Wang AS; van Heteren J; Vo NJ; Schmidt TG
Med Phys; 2022 May; 49(5):3523-3528. PubMed ID: 35067940
[TBL] [Abstract][Full Text] [Related]
5. Percutaneous osteosynthesis in the pelvis in cancer patients.
Deschamps F; de Baere T; Hakime A; Pearson E; Farouil G; Teriitehau C; Tselikas L
Eur Radiol; 2016 Jun; 26(6):1631-9. PubMed ID: 26318372
[TBL] [Abstract][Full Text] [Related]
6. Automatic multi-class intertrochanteric femur fracture detection from CT images based on AO/OTA classification using faster R-CNN-BO method.
Yoon SJ; Hyong Kim T; Joo SB; Eel Oh S
J Appl Biomed; 2020 Dec; 18(4):97-105. PubMed ID: 34907762
[TBL] [Abstract][Full Text] [Related]
7. Computed tomography for occult fractures of the proximal femur, pelvis, and sacrum in clinical practice: single institution, dual-site experience.
Mandell JC; Weaver MJ; Khurana B
Emerg Radiol; 2018 Jun; 25(3):265-273. PubMed ID: 29327108
[TBL] [Abstract][Full Text] [Related]
8. Original research: utilization of a convolutional neural network for automated detection of lytic spinal lesions on body CTs.
Chang CY; Huber FA; Yeh KJ; Buckless C; Torriani M
Skeletal Radiol; 2023 Jul; 52(7):1377-1384. PubMed ID: 36651936
[TBL] [Abstract][Full Text] [Related]
9. BRR-Net: A tandem architectural CNN-RNN for automatic body region localization in CT images.
Agrawal V; Udupa J; Tong Y; Torigian D
Med Phys; 2020 Oct; 47(10):5020-5031. PubMed ID: 32761899
[TBL] [Abstract][Full Text] [Related]
10. A deep learning-based approach to automatic proximal femur segmentation in quantitative CT images.
Deng Y; Wang L; Zhao C; Tang S; Cheng X; Deng HW; Zhou W
Med Biol Eng Comput; 2022 May; 60(5):1417-1429. PubMed ID: 35322343
[TBL] [Abstract][Full Text] [Related]
11. The effect of deep convolutional neural networks on radiologists' performance in the detection of hip fractures on digital pelvic radiographs.
Mawatari T; Hayashida Y; Katsuragawa S; Yoshimatsu Y; Hamamura T; Anai K; Ueno M; Yamaga S; Ueda I; Terasawa T; Fujisaki A; Chihara C; Miyagi T; Aoki T; Korogi Y
Eur J Radiol; 2020 Sep; 130():109188. PubMed ID: 32721827
[TBL] [Abstract][Full Text] [Related]
12. Complete abdomen and pelvis segmentation using U-net variant architecture.
Weston AD; Korfiatis P; Philbrick KA; Conte GM; Kostandy P; Sakinis T; Zeinoddini A; Boonrod A; Moynagh M; Takahashi N; Erickson BJ
Med Phys; 2020 Nov; 47(11):5609-5618. PubMed ID: 32740931
[TBL] [Abstract][Full Text] [Related]
13. External validation of a convolutional neural network algorithm for opportunistically detecting vertebral fractures in routine CT scans.
Nicolaes J; Liu Y; Zhao Y; Huang P; Wang L; Yu A; Dunkel J; Libanati C; Cheng X
Osteoporos Int; 2024 Jan; 35(1):143-152. PubMed ID: 37674097
[TBL] [Abstract][Full Text] [Related]
14. Prediction of fracture load and stiffness of the proximal femur by CT-based specimen specific finite element analysis: cadaveric validation study.
Miura M; Nakamura J; Matsuura Y; Wako Y; Suzuki T; Hagiwara S; Orita S; Inage K; Kawarai Y; Sugano M; Nawata K; Ohtori S
BMC Musculoskelet Disord; 2017 Dec; 18(1):536. PubMed ID: 29246133
[TBL] [Abstract][Full Text] [Related]
15. Technical note: Phantom-based training framework for convolutional neural network CT noise reduction.
Huber NR; Missert AD; Gong H; Leng S; Yu L; McCollough CH
Med Phys; 2023 Feb; 50(2):821-830. PubMed ID: 36385704
[TBL] [Abstract][Full Text] [Related]
16. Automated segmentation of the femur and pelvis from 3D CT data of diseased hip using hierarchical statistical shape model of joint structure.
Yokota F; Okada T; Takao M; Sugano N; Tada Y; Sato Y
Med Image Comput Comput Assist Interv; 2009; 12(Pt 2):811-8. PubMed ID: 20426186
[TBL] [Abstract][Full Text] [Related]
17. Risk assessment of femoral pathological fracture in prostate cancer patients by computed tomography analysis.
Rikitake H; Horiuchi K; Miyai K; Susa M; Inoue M; Taguchi E; Ishizaka T; Chiba K
J Bone Miner Metab; 2022 Jul; 40(4):704-711. PubMed ID: 35637395
[TBL] [Abstract][Full Text] [Related]
18. Learning low-dose CT degradation from unpaired data with flow-based model.
Liu X; Liang X; Deng L; Tan S; Xie Y
Med Phys; 2022 Dec; 49(12):7516-7530. PubMed ID: 35880375
[TBL] [Abstract][Full Text] [Related]
19. Development of an open-source measurement system to assess the areal bone mineral density of the proximal femur from clinical CT images.
Uemura K; Otake Y; Takao M; Makino H; Soufi M; Iwasa M; Sugano N; Sato Y
Arch Osteoporos; 2022 Jan; 17(1):17. PubMed ID: 35038079
[TBL] [Abstract][Full Text] [Related]
20. Prediction of pathological fracture in patients with lower limb bone metastasis using computed tomography imaging.
Shinoda Y; Sawada R; Ishibashi Y; Akiyama T; Zhang L; Hirai T; Oka H; Ohki T; Ikegami M; Okajima K; Okuma T; Kobayashi H; Goto T; Haga N; Tanaka S
Clin Exp Metastasis; 2020 Oct; 37(5):607-616. PubMed ID: 32737738
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
