Effective integration of object boundaries and regions for improving the performance of medical image segmentation by using two cascaded networks

Patil, 2013, Medical image segmentation: a review, Int. J. Comput. Sci. Mob. Comput., 2, 22 Abduh, 2016, Robust computer-aided detection of pulmonary nodules from chest computed tomography, J. Med. Imag. Health Inform., 3, 1 Donahue, 2013, DeCAF: a deep convolutional activation feature for generic visual recognition, Comput. Sci., 50, 815 LeCun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539 Shen, 2017, Deep learning in medical image analysis, Annu. Rev. Biomed. Eng., 19, 221, 10.1146/annurev-bioeng-071516-044442 Suzuki, 2017, Overview of deep learning in medical imaging, Radiol. Phys. Technol., 10, 257, 10.1007/s12194-017-0406-5 Krizhevsky, 2012, ImageNet classification with deep convolutional neural networks, 1097 Dhungel, 2015, Deep learning and structured prediction for the segmentation of mass in mammograms, 605 Xing, 2016, An automatic learning-based framework for robust nucleus segmentation, IEEE Trans. Med. Imag., 35, 550, 10.1109/TMI.2015.2481436 Long, 2014, Fully Convolutional networks for semantic segmentation, IEEE Trans. Patt. Analy. Mach. Intell., 39, 640 Zhou, 2016, Three-dimensional CT image segmentation by combining 2D fully convolutional network with 3D majority voting, 111 Korez, 2016, Model-based segmentation of vertebral bodies from MR images with 3D CNNs, 433 Moeskops, 2016, Deep learning for multi-task medical image segmentation in multiple modalities, 478 Ronneberger, 2015, U-Net:convolutional networks for biomedical image segmentation, 234 Badrinarayanan, 2017, SegNet: a deep convolutional encoder-decoder architecture for image segmentation, IEEE Trans. Pattern Analy. Mach. Intell., 12, 2481, 10.1109/TPAMI.2016.2644615 O. Çiçek, A. Abdulkadir, S. Lienkamp, T. Brox, O. Ronneberger: 3D U-Net:Learning Dense Volumetric Segmentation from Sparse Annotation. International Conference on Medical Image Computing and Computer-Assisted Intervention 424-432, 2016. Milletari, 2016, V-Net: fully convolutional neural networks for volumetric medical image segmentation, 565 Drozdzal, 2016, The importance of skip connections in biomedical image segmentation, 179 Zhou, 2018, UNet++: a nested U-Net architecture for medical image segmentation. deep learning in medical image analysis and multimodal learning for clinical decision support, Lect. Notes Comput. Sci., 11045, 3, 10.1007/978-3-030-00889-5_1 Xie, 2020, Accurate medical image segmentation by predicting and correcting errors, IEEE Trans. Med. Imag., 40, 286, 10.1109/TMI.2020.3025308 Soberanis, 2020, An uncertainty-driven GCN refinement strategy for organ segmentation, J. Mach. Learn. Biomed. Imag. Spec. Issue, 1, 1 Cao, 2021, Cascaded SE-ResUnet for segmentation of thoracic organs at risk, Neurocomputing, 453, 357, 10.1016/j.neucom.2020.08.086 Trullo, 2017, Segmentation of organs at risk in thoracic CT images using a sharpmask architecture and conditional random fields, 1003 Shiraishi, 2000, Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists’ detection of pulmonary nodules, AJR, 174, 71, 10.2214/ajr.174.1.1740071 Ginneken, 2006, Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public data, Med. Image Anal., 10, 19, 10.1016/j.media.2005.02.002 Sivaswamy, 2015, Ujjwal: a comprehensive retinal image dataset for the assessment of glaucoma from the optic nerve head analysis, JSM Biomed. Imag. Data Pap., 2, 1004 Gu, 2019, Segmentation and suppression of pulmonary vessels in low-dose Chest CT Scans, Med. Phys., 46, 13648, 10.1002/mp.13648 He, 2015, Delving deep into rectifiers: surpassing human-level performance on imagenet classification, 1026