Deep learning in CT image segmentation of cervical cancer: a systematic review and meta-analysis

[1] Bray F(2018)Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries CA Cancer J Clin 68 394-424

[2] Ferlay J(2017)Cancer incidence and mortality among young adults aged 20–39 years worldwide in 2012: a population-based study Lancet Oncol 18 1579-1589

[3] Soerjomataram I(2019)Deep learning in medical imaging and radiation therapy Med Phys 46 e1-e36

[4] Siegel RL(2020)Evaluation of deep learning-based auto-segmentation algorithms for delineating clinical target volume and organs at risk involving data for 125 cervical cancer patients J Appl Clin Med Phys 21 272-279

[5] Torre LA(2020)Automatic contouring system for cervical cancer using convolutional neural networks Med Phys 47 5648-5658

[6] Jemal A(2020)Development and validation of a deep learning algorithm for auto-delineation of clinical target volume and organs at risk in cervical cancer radiotherapy Radiother Oncol 153 172-179

[7] Fidler MM(2021)Automatic clinical target volume delineation for cervical cancer in CT images using deep learning Med Phys 48 3968-3981

[8] Gupta S(2020)Segmentation of organs-at-risk in cervical cancer CT images with a convolutional neural network Phys Med 69 184-191

[9] Soerjomataram I(2021)Automatic segmentation using deep learning to enable online dose optimization during adaptive radiation therapy of cervical cancer Int J Radiat Oncol Biol Phys 109 1096-1110

[10] Ferlay J(2021)Evolution of deep learning algorithms for MRI-based brain tumor image segmentation Crit Rev Biomed Eng 49 77-94

[1] Bray F(2018)Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries CA Cancer J Clin 68 394-424

[2] Ferlay J(2017)Cancer incidence and mortality among young adults aged 20–39 years worldwide in 2012: a population-based study Lancet Oncol 18 1579-1589

[3] Soerjomataram I(2019)Deep learning in medical imaging and radiation therapy Med Phys 46 e1-e36

[4] Siegel RL(2020)Evaluation of deep learning-based auto-segmentation algorithms for delineating clinical target volume and organs at risk involving data for 125 cervical cancer patients J Appl Clin Med Phys 21 272-279

[5] Torre LA(2020)Automatic contouring system for cervical cancer using convolutional neural networks Med Phys 47 5648-5658

[6] Jemal A(2020)Development and validation of a deep learning algorithm for auto-delineation of clinical target volume and organs at risk in cervical cancer radiotherapy Radiother Oncol 153 172-179

[7] Fidler MM(2021)Automatic clinical target volume delineation for cervical cancer in CT images using deep learning Med Phys 48 3968-3981

[8] Gupta S(2020)Segmentation of organs-at-risk in cervical cancer CT images with a convolutional neural network Phys Med 69 184-191

[9] Soerjomataram I(2021)Automatic segmentation using deep learning to enable online dose optimization during adaptive radiation therapy of cervical cancer Int J Radiat Oncol Biol Phys 109 1096-1110

[10] Ferlay J(2021)Evolution of deep learning algorithms for MRI-based brain tumor image segmentation Crit Rev Biomed Eng 49 77-94