Strategies to improve deep learning-based salivary gland segmentation

[1] Brouwer CL(2012)3D variation in delineation of head and neck organs at risk Radiat Oncol 7 32-90

[2] Steenbakkers RJ(2015)CT-based delineation of organs at risk in the head and neck region: DAHANCA, EORTC, GORTEC, HKNPCSG, NCIC CTG, NCRI, NRG Oncology and TROG consensus guidelines Radiother Oncol 117 83-S63

[3] van den Heuvel E(2010)Radiotherapy dose-volume effects on salivary gland function Int J Radiat Oncol Biol Phys 76 S58-684

[4] Brouwer CL(2019)Deep learning-based delineation of head and neck organs-at-risk: geometric and dosimetric evaluation Int J Radiat Oncol Biol Phys 104 677-394

[5] Steenbakkers RJ(2006)Symmetry of the submandibular glands in humans: a postmortem study assessing the linear morphometric parameters Oral Surg Oral Med Oral Pathol Oral Radiol Endod 102 391-1958

[6] Bourhis J(2014)Dropout: a simple way to prevent neural networks from overfitting J Mach Learn Res 15 1929-984

[7] Deasy JO(2017)Differential data augmentation techniques for medical imaging classification tasks AMIA Annu Symp Proc 2017 979-196

[8] Moiseenko V(2018)Is accurate contouring of salivatory and swallowing structures necessary to spare them in head and neck VMAT plans? Radiother Oncol 127 190-1001

[9] Marks L(1990)Neural network ensembles Trans Pattern Anal Mach Intell 12 993-S282

[10] Van Rooij W(2018)Evaluation of deep learning methods for parotid gland segmentation J Med Imaging 127 S281-undefined

[1] Brouwer CL(2012)3D variation in delineation of head and neck organs at risk Radiat Oncol 7 32-90

[2] Steenbakkers RJ(2015)CT-based delineation of organs at risk in the head and neck region: DAHANCA, EORTC, GORTEC, HKNPCSG, NCIC CTG, NCRI, NRG Oncology and TROG consensus guidelines Radiother Oncol 117 83-S63

[3] van den Heuvel E(2010)Radiotherapy dose-volume effects on salivary gland function Int J Radiat Oncol Biol Phys 76 S58-684

[4] Brouwer CL(2019)Deep learning-based delineation of head and neck organs-at-risk: geometric and dosimetric evaluation Int J Radiat Oncol Biol Phys 104 677-394

[5] Steenbakkers RJ(2006)Symmetry of the submandibular glands in humans: a postmortem study assessing the linear morphometric parameters Oral Surg Oral Med Oral Pathol Oral Radiol Endod 102 391-1958

[6] Bourhis J(2014)Dropout: a simple way to prevent neural networks from overfitting J Mach Learn Res 15 1929-984

[7] Deasy JO(2017)Differential data augmentation techniques for medical imaging classification tasks AMIA Annu Symp Proc 2017 979-196

[8] Moiseenko V(2018)Is accurate contouring of salivatory and swallowing structures necessary to spare them in head and neck VMAT plans? Radiother Oncol 127 190-1001

[9] Marks L(1990)Neural network ensembles Trans Pattern Anal Mach Intell 12 993-S282

[10] Van Rooij W(2018)Evaluation of deep learning methods for parotid gland segmentation J Med Imaging 127 S281-undefined