Stacked dilated convolutions and asymmetric architecture for U-Net-based medical image segmentation

Litjens, 2017, A survey on deep learning in medical image analysis, Med. Image Anal., 42, 60, 10.1016/j.media.2017.07.005 Asgari Taghanaki, 2021, Deep semantic segmentation of natural and medical images: a review, Artif. Intell. Rev., 54, 137, 10.1007/s10462-020-09854-1 Singh, 2017, Segmentation of prostate contours for automated diagnosis using ultrasound images: A survey, J. Comput. Sci., 21, 223, 10.1016/j.jocs.2017.04.016 Wang, 2020, Adversarial convolutional network for esophageal tissue segmentation on OCT images, Biomed. Opt. Express, 11, 3095, 10.1364/BOE.394715 Shen, 2021, Automatic segmentation of the femur and tibia bones from X-ray images based on pure dilated residual U-Net, Inverse Probl. Imaging, 15, 1333, 10.3934/ipi.2020057 Looney, 2018, Fully automated, real-time 3D ultrasound segmentation to estimate first trimester placental volume using deep learning, JCI Insight, 3, 10.1172/jci.insight.120178 Sommersperger, 2021, Real-time tool to layer distance estimation for robotic subretinal injection using intraoperative 4D OCT, Biomed. Opt. Express, 12, 1085, 10.1364/BOE.415477 Wang, 2019, Quantification of full left ventricular metrics via deep regression learning with contour-guidance, IEEE Access, 7, 47918, 10.1109/ACCESS.2019.2907564 Anas, 2018, A deep learning approach for real time prostate segmentation in freehand ultrasound guided biopsy, Med. Image Anal., 48, 107, 10.1016/j.media.2018.05.010 Keller, 2018, Real-time corneal segmentation and 3D needle tracking in intrasurgical OCT, Biomed. Opt. Express, 9, 2716, 10.1364/BOE.9.002716 Ronneberger, 2015, U-Net: Convolutional networks for biomedical image segmentation, 234 Du, 2020, Medical image segmentation based on U-Net: A review, J. Imaging Sci. Technol., 64, 1, 10.2352/J.ImagingSci.Technol.2020.64.2.020508 Oktay, 2018 Alom, 2018 Zhou, 2018, UNet++: A nested U-Net architecture for medical image segmentation, 3 Zhang, 2021, Automatic segmentation of the cardiac MR images based on nested fully convolutional dense network with dilated convolution, Biomed. Signal Process. Control, 68, 10.1016/j.bspc.2021.102684 Çiçek, 2016, 3D U-Net: learning dense volumetric segmentation from sparse annotation, 424 Chen, 2021 Sixiao Zheng, Jiachen Lu, Hengshuang Zhao, Xiatian Zhu, Zekun Luo, Yabiao Wang, Yanwei Fu, Jianfeng Feng, Tao Xiang, Philip H.S. Torr, et al., Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 6881–6890. Isensee, 2018, No new-net, 234 Isensee, 2018 Isensee, 2021, nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation, Nature Methods, 18, 203, 10.1038/s41592-020-01008-z Devalla, 2018, DRUNET: a dilated-residual U-Net deep learning network to segment optic nerve head tissues in optical coherence tomography images, Biomed. Opt. Express, 9, 3244, 10.1364/BOE.9.003244 Hamaguchi, 2018, Effective use of dilated convolutions for segmenting small object instances in remote sensing imagery, 1442 Yu, 2015 Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, Hartwig Adam, Encoder-decoder with atrous separable convolution for semantic image segmentation, in: Proceedings of the European Conference on Computer Vision, ECCV, 2018, pp. 801–818. René Schuster, Oliver Wasenmuller, Christian Unger, Didier Stricker, SDC-stacked dilated convolution: A unified descriptor network for dense matching tasks, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 2556–2565. Wojna, 2019, The devil is in the decoder: Classification, regression and gans, Int. J. Comput. Vis., 127, 1694, 10.1007/s11263-019-01170-8 Sudre, 2017, Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations, 240 Yuan, 2017, Automatic skin lesion segmentation using deep fully convolutional networks with jaccard distance, IEEE Trans. Med. Imaging, 36, 1876, 10.1109/TMI.2017.2695227 Codella, 2018, Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic), 168 Baumgartner, 2017, An exploration of 2D and 3D deep learning techniques for cardiac MR image segmentation, 111 Alexander Kirillov, Ross Girshick, Kaiming He, Piotr Dollár, Panoptic feature pyramid networks, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 6399–6408. Xue, 2018, Adversarial learning with multi-scale loss for skin lesion segmentation, 859 Lei, 2020, Skin lesion segmentation via generative adversarial networks with dual discriminators, Med. Image Anal., 64, 10.1016/j.media.2020.101716 Xie, 2020, Skin lesion segmentation using high-resolution convolutional neural network, Comput. Methods Programs Biomed., 186, 10.1016/j.cmpb.2019.105241 Zafar, 2020, Skin lesion segmentation from dermoscopic images using convolutional neural network, Sensors, 20, 1601, 10.3390/s20061601 Al-Masni, 2018, Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks, Comput. Methods Programs Biomed., 162, 221, 10.1016/j.cmpb.2018.05.027 Mamalet, 2012, Simplifying convnets for fast learning, 58 François Chollet, Xception: Deep learning with depthwise separable convolutions, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 1251–1258.