PURPOSE: Manual delineation of organs-at-risk (OARs) in radiotherapy is both time-consuming and subjective. Automated and more accurate segmentation is of the utmost importance in clinical application. The purpose of this study is to further improve the segmentation accuracy and efficiency with a novel network named convolutional neural networks (CNN) Cascades. METHODS: CNN Cascades was a two-step, coarse-to-fine approach that consisted of a simple region detector (SRD) and a fine segmentation unit (FSU). The SRD first used a relative shallow network to define the region of interest (ROI) where the organ was located, and then, the FSU took the smaller ROI as input and adopted a deep network for fine segmentation. The imaging data (14,651 slices) of 100 head-and-neck patients with segmentations were used for this study. The performance was compared with the state-of-the-art single CNN in terms of accuracy with metrics of Dice similarity coefficient (DSC) and Hausdorff distance (HD) values. RESULTS: The proposed CNN Cascades outperformed the single CNN on accuracy for each OAR. Similarly, for the average of all OARs, it was also the best with mean DSC of 0.90 (SRD: 0.86, FSU: 0.87, and U-Net: 0.85) and the mean HD of 3.0 mm (SRD: 4.0, FSU: 3.6, and U-Net: 4.4). Meanwhile, the CNN Cascades reduced the mean segmentation time per patient by 48% (FSU) and 5% (U-Net), respectively. CONCLUSIONS: The proposed two-step network demonstrated superior performance by reducing the input region. This potentially can be an effective segmentation method that provides accurate and consistent delineation with reduced clinician interventions for clinical applications as well as for quality assurance of a multicenter clinical trial.
PURPOSE: Manual delineation of organs-at-risk (OARs) in radiotherapy is both time-consuming and subjective. Automated and more accurate segmentation is of the utmost importance in clinical application. The purpose of this study is to further improve the segmentation accuracy and efficiency with a novel network named convolutional neural networks (CNN) Cascades. METHODS: CNN Cascades was a two-step, coarse-to-fine approach that consisted of a simple region detector (SRD) and a fine segmentation unit (FSU). The SRD first used a relative shallow network to define the region of interest (ROI) where the organ was located, and then, the FSU took the smaller ROI as input and adopted a deep network for fine segmentation. The imaging data (14,651 slices) of 100 head-and-neck patients with segmentations were used for this study. The performance was compared with the state-of-the-art single CNN in terms of accuracy with metrics of Dice similarity coefficient (DSC) and Hausdorff distance (HD) values. RESULTS: The proposed CNN Cascades outperformed the single CNN on accuracy for each OAR. Similarly, for the average of all OARs, it was also the best with mean DSC of 0.90 (SRD: 0.86, FSU: 0.87, and U-Net: 0.85) and the mean HD of 3.0 mm (SRD: 4.0, FSU: 3.6, and U-Net: 4.4). Meanwhile, the CNN Cascades reduced the mean segmentation time per patient by 48% (FSU) and 5% (U-Net), respectively. CONCLUSIONS: The proposed two-step network demonstrated superior performance by reducing the input region. This potentially can be an effective segmentation method that provides accurate and consistent delineation with reduced clinician interventions for clinical applications as well as for quality assurance of a multicenter clinical trial.
Authors: Kenneth Clark; Bruce Vendt; Kirk Smith; John Freymann; Justin Kirby; Paul Koppel; Stephen Moore; Stanley Phillips; David Maffitt; Michael Pringle; Lawrence Tarbox; Fred Prior Journal: J Digit Imaging Date: 2013-12 Impact factor: 4.056
Authors: Stuart Y Tsuji; Andrew Hwang; Vivian Weinberg; Sue S Yom; Jeanne M Quivey; Ping Xia Journal: Int J Radiat Oncol Biol Phys Date: 2010-03-16 Impact factor: 7.038
Authors: Valerio Fortunati; René F Verhaart; Fedde van der Lijn; Wiro J Niessen; Jifke F Veenland; Margarethus M Paulides; Theo van Walsum Journal: Med Phys Date: 2013-07 Impact factor: 4.071
Authors: X Allen Li; An Tai; Douglas W Arthur; Thomas A Buchholz; Shannon Macdonald; Lawrence B Marks; Jean M Moran; Lori J Pierce; Rachel Rabinovitch; Alphonse Taghian; Frank Vicini; Wendy Woodward; Julia R White Journal: Int J Radiat Oncol Biol Phys Date: 2009-03-01 Impact factor: 7.038
Authors: Xinyuan Chen; Kuo Men; Bo Chen; Yu Tang; Tao Zhang; Shulian Wang; Yexiong Li; Jianrong Dai Journal: Front Oncol Date: 2020-04-28 Impact factor: 6.244
Authors: Carlos E Cardenas; Beth M Beadle; Adam S Garden; Heath D Skinner; Jinzhong Yang; Dong Joo Rhee; Rachel E McCarroll; Tucker J Netherton; Skylar S Gay; Lifei Zhang; Laurence E Court Journal: Int J Radiat Oncol Biol Phys Date: 2020-10-14 Impact factor: 8.013