Deep Learning Based Methods for Biomedical Image Segmentation: A Review

Year 2023, Volume: 12 Issue: 1, 161 - 187, 10.06.2023

Tuğba Şentürk , Fatma Latifoğlu

https://doi.org/10.55007/dufed.1181996

Cited By: 2

Abstract

A deep learning model is a model in the field of medical imaging that provides more contributions in terms of time and performance compared to existing methods. It includes automatic segmentation or classification of images. While existing methods process single-layer images, with the deep learning model, higher performance and more accurate results can be obtained on multi-layer images. Recent developments show that these approaches are highly effective in identifying and quantifying patterns in medical images. The most important reason for these advances is the core function of deep learning approaches to directly obtain hierarchical feature representations from images. Therefore, the applications of deep learning methods to medical image processing and segmentation are rapidly becoming the latest technology and resulting in performance improvements in clinical applications. This article provides an overview of the applications, methods, and contents of deep learning approaches for the segmentation of biomedical images.

Keywords

Imaging, Biomedical image, Deep learning, Biomedical image segmentation, Medical image processing

Biyomedikal Görüntülerin Bölütlenmesine Yönelik Derin Öğrenmeye Dayalı Yöntemler: Bir Gözden Geçirme

Abstract

Tıbbi görüntüleme alanında derin öğrenme modeli, mevcut yöntemlere kıyasla zaman ve performans açısından daha fazla katkıda bulunan bir modeldir. Görüntülerin otomatik olarak bölütlenmesini veya sınıflandırılmasını kapsar. Mevcut yöntemler ile tek katmanlı görüntüler üzerinden işlem yapılırken, derin öğrenme modeli ile çok katmanlı görüntüler üzerinden çalışma performansı daha yüksek ve daha kesin sonuçlar elde edilebilir. Son zamanlardaki gelişmeler, bu yaklaşımların tıbbi görüntülerdeki örüntülerin tanımlanması ve nicelendirilmesinde oldukça etkili olduğunu göstermektedir. Bu ilerlemelerin en önemli nedeni, derin öğrenme yaklaşımlarının doğrudan görüntülerden hiyerarşik özellik temsilleri elde etme yeteneğidir. Bu nedenle, derin öğrenme yöntemlerinin tıbbi görüntü işleme ve bölütleme alanındaki uygulamaları hızla en son teknolojiye dönüşmektedir ve klinik uygulamalarda performans iyileştirmeleri sağlamaktadır. Bu makalede, derin öğrenme yaklaşımlarının biyomedikal görüntülerin bölütlenmesi için uygulamaları, yöntemleri ve içerikleri genel bir bakış açısıyla incelenmiştir.

Keywords

Görüntüleme, Biyomedikal görüntüler, Derin öğrenme, Biyomedikal görüntü bülütleme, Tıbbi görüntü işleme

References

• A. Maier, C. Syben, T. Lasser and C. Riess, “A gentle introduction to deep learning in medical image processing,” Elsevier Z. für M. Physik, vol. 29, no. 2, pp. 86-101, 2019, doi: 10.1016/j.zemedi.2018.12.003.
• M. Sharif, S. Mohsin and M. J. Jamal, “Illumination normalization preprocessing for face recognition,” 2010 The 2nd Conference on Environmental Science and Information Application Technology, Wuhan, China, July 17-18, 2010.
• S. Aja-Fernández, A. H. Curiale, and G. Vegas-Sánchez-Ferrero, “A local fuzzy thresholding methodology for multiregion image segmentation,” Knowl Based Syst, vol. 83, no. 1, pp. 1–12, 2015, doi: 10.1016/J.KNOSYS.2015.02.029.
• P. P. Vijay and N. C. Patil, “Gray scale image segmentation using OTSU Thresholding optimal approach,” Journal for Research, vol. 2, no. 5, pp. 2395-7549, 2016.
• S. S. Al-amri, N. V. Kalyankar, and S. D. Khamitkar, “Image Segmentation by Using Threshold Techniques,” Lahore Garrison University Research Journal of Computer Science and Information Technology, vol. 2, no. 2, pp. 83-86, 2010, doi: 10.48550/arxiv.1005.4020.
• G. B. Coleman and H. C. Andrews, “Image segmentation by clustering,” Proceedings of the IEEE, vol. 67, no. 5, pp. 773-785, 1979, doi: 10.1109/PROC.1979.11327
• G. Sethi, B. S. Saini and D. Singh, “Segmentation of cancerous regions in liver using an edge-based and phase congruent region enhancement method,” Elsevier, Computers & Electrical Engineering, vol. 53, pp. 244-262, 2016, doi: doi.org/10.1016/j.compeleceng.2015.06.025
• K. Wu and D. Zhang, “Robust tongue segmentation by fusing region-based and edge-based approaches,” Expert Syst Appl, vol. 42, no. 21, pp. 8027–8038, Jul. 2015, doi: 10.1016/J.ESWA.2015.06.032.
• N. M. Zaitoun and M. J. Aqel, “Survey on Image Segmentation Techniques,” Procedia Comput Sci, vol. 65, pp. 797–806, 2015, doi: 10.1016/J.PROCS.2015.09.027.
• S. Niu, Q. Chen, L. de Sisternes, Z. Ji, Z. Zhou, and D. L. Rubin, “Robust noise region-based active contour model via local similarity factor for image segmentation,” Pattern Recognit, vol. 61, pp. 104–119, 2017, doi: 10.1016/J.PATCOG.2016.07.022.
• E. Anjna and R. Rajandeep, “Review of image segmentation technique,” International Journal of Advanced Research in Computer Science, vol. 8, no. 4, pp. 36-39, 2017.
• A. Krizhevsky, I. Sutskever and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Communications of the ACM, vol. 60, no. 6, pp. 84-90, 2017, doi: https://doi.org/10.1145/3065386.
• G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath and B. Kingsbury, “Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups,” IEEE Signal Processing Magazine, vol. 29, no. 6, pp. 82-97, 2012, doi: 10.1109/MSP.2012.2205597.
• I. Sutskever, O. Vinyals, and Q. V. le, “Sequence to Sequence Learning with Neural Networks,” Adv Neural Inf Process Syst, vol. 27, 2014.
• W. Jifara, F. Jiang, S. Rho, M. Cheng, and S. Liu, “Medical image denoising using convolutional neural network: a residual learning approach,” Journal of Supercomputing, vol. 75, no. 2, pp. 704–718, 2019, doi: https://doi.org/10.1007/s11227-017-2080-0.
• P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio and P. A. Manzagol, “Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion.” Journal of Machine Learning Research, vol. 11, no. 12, pp. 3371–3408, 2010.
• K. Zhang, W. Zuo, Y. Chen, D. Meng and L. Zhang “Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising,” IEEE Transactions on Image Processing, vol. 26, no. 7, pp. 3142-3155, 2017, doi: 10.1109/TIP.2017.2662206.
• K. He, X. Zhang, S. Ren and J. Sun, “Deep Residual Learning for Image Recognition,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, Jun. 27-30, 2016.
• K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” ICLR 2015, arXiv: 1409.1556.
• L. Chen, G. Papandreou, I. Kokkinos, K. Murphy and A. L. Yuille, “Semantic image segmentation with deep convolutional nets and fully connected crfs,” IEEE Trans Pattern Anal Mach Intelligence, vol. 40, no. 4, pp. 834-848, 2018, doi: 10.1109/TPAMI.2017.2699184.
• H. Noh, S. Hong and B. Han, “Learning deconvolution network for semantic segmentation,” 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, Dec. 04-13, 2015.
• J. Hung, A. Goodman, D. Ravel, S. C. P. Lopes, G. W. Rangel, O. A. Nery, B. Malleret, F. Nosten, M. V. G. Lacerda, M. U. Ferreira, L. Rénia, M. T. Duraisingh, F. T. M. Costa, M. Marti and A. E. Carpenter, “Keras R-CNN: Library for cell detection in biological images using deep neural networks,” BMC Bioinformatics, vol. 21, no. 1, 2020, doi: 10.1186/S12859-020-03635-X.
• Zhao, Z. Zhao, P. Zheng, S. Xu and X. Wu, “Object detection with deep learning: A review,” EEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 11, pp. 3212-3232, 2019, doi: 10.1109/TNNLS.2018.2876865
• F. H. D. Araújo, R. R.V. Silva, D. M. Ushizima, M. T. Rezende, C. M. Carneiro, A. G. C. Bianchi and F. N. S. Medeiros, “Deep learning for cell image segmentation and ranking,” Computerized Medical Imaging and Graphics, vol. 72, pp. 13-21, 2019, doi: https://doi.org/10.1016/j.compmedimag.2019.01.003.
• S. Minaee, Y. Boykov, F. Porikli, A. Plaza, N. Kehtarnazav and D. Terzopoulos, “Image segmentation using deep learning: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 7, pp. 3523-3542, 2022, doi: 10.1109/TPAMI.2021.3059968.
• B. M. Priego-Torres, D. Sanchez-Morillo, M. A. Fernandez-Granero and M. Garcia-Rojo “Automatic segmentation of whole-slide H&E stained breast histopathology images using a deep convolutional neural network architecture,” Expert Systems with Applications, vol. 151, pp. 113387, 2020, doi: https://doi.org/10.1016/j.eswa.2020.113387
• T. Zhou, S. Ruan and S. Canu, “A review: Deep learning for medical image segmentation using multi-modality fusion,” Array, vol. 3–4, pp. 100004, 2019, doi: https://doi.org/10.1016/j.array.2019.100004.
• A. Garcia-Garcia, S. Orts-Escolano, S. O. Oprea, V. Villena-Martinez, and J. Garcia-Rodriguez, “A review on deep learning techniques applied to semantic segmentation,” arXiv: 1704.06857.,22 Apr 2017.
• Y. Weng, T. Zhou, Y. Li and X. Qiu, “Nas-unet: Neural architecture search for medical image segmentation,” Special Section On Advanced Optical Imaging For Extreme Environments, vol.7, pp. 44247-44257, 2019, doi: 10.1109/ACCESS.2019.2908991.
• Z. Bozdağ Karakeçi and M. Fatih Talu, “Histopatolojik Görüntülerde Kanser Tespit ve Lokasyon Yöntemleri,” Avrupa Bilim ve Teknoloji Dergisi, no. 23, pp. 608–616, 2021, doi: 10.31590/ejosat.888836.
• E. Menteşe and E. Hançer, “Histopatoloji görüntülerde derin öğrenme yöntemleri ile çekirdek segmentasyonu,” Avrupa Bilim ve Teknoloji Dergisi, Ejosat, Özel Sayı, pp. 95–102, 2020, doi: 10.31590/ejosat.819409.
• Y. Guo, Y. Liu, T. Georgiou, and M. S. Lew, “A review of semantic segmentation using deep neural networks,” Int J Multimed Inf Retr, vol. 7, no. 2, pp. 87–93, Jun. 2018, doi: 10.1007/S13735-017-0141-Z.
• Y. Fu, Y. Lei, T. Wang, W. J. Curran, T. Liu and X. Yang., “A review of deep learning based methods for medical image multi-organ segmentation,” Physica Medica, vol. 85, pp. 107-122, 2021, doi: https://doi.org/10.1016/j.ejmp.2021.05.003
• A. Fischer, K. Jacobson, J. Rose and R. Zeller, “Hematoxylin and eosin staining of tissue and cell sections,” Cold spring harbor protocols, Preparation of Cells and Tissues for Fluorescence Microscopy, Chapter 4, in Basic Methods in Microscopy NY, USA, 2008.
• I. Rizwan I. Haque and J. Neubert, “Deep learning approaches to biomedical image segmentation,” Inform Med Unlocked, vol. 18, pp. 100297, 2020, doi: 10.1016/J.IMU.2020.100297.
• S. Minaee, Y. Buykov, F. Porkli, A. Plaza, N. Kehtarnavaz and D. Terzopoulos, “Medical Image Segmentation Using Deep Learning: A Survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 7, pp. 3523-3542, 2022, doi: 10.1109/TPAMI.2021.3059968.
• Ş. Öztürk and B. Akdemir, “Cell-type based semantic segmentation of histopathological images using deep convolutional neural networks,” Int J Imaging Syst Technol, vol. 29, no. 3, pp. 234–246, 2019, doi: 10.1002/IMA.22309.
• J. Âmin, M. Sharif, N. Gul, M. Raza, M. A. Anjum, M. W. Nisar and S. A. C. Bukhari “Brain Tumor Detection by Using Stacked Autoencoders in Deep Learning,” J Med Syst, vol. 44, no. 2, 2020, doi: 10.1007/S10916-019-1483-2.
• H. Shin, M. Orton, D. J. Collins, S. J. Doran and M. O. Leach, “Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp. 1930-1943, 2013, doi: 10.1109/TPAMI.2012.277.
• P. Vincent, H. Larochelle, Y. Bengio, and P. A. Manzagol, “Extracting and composing robust features with denoising autoencoders,” Proceedings of the 25th International Conference on Machine Learning, Helsinki, Finland, jul. 5, 2008.
• Z. Fan, D. Bi, L. He, M. Shiping, S. Gao, and C. Li, “Low-level structure feature extraction for image processing via stacked sparse denoising autoencoder,” Neurocomputing, vol. 243, pp. 12–20, 2017, doi: 10.1016/J.NEUCOM.2017.02.066.
• V. Alex, K. Vaidhya, S. Thirunavukkarasu, C. Kesavadas and G. Krishnamurthi, “Semisupervised learning using denoising autoencoders for brain lesion detection and segmentation,” Journal of Medical Imaging, vol. 4, no. 4, pp. 041311, 2017, doi: https://doi.org/10.1117/1.JMI.4.4.041311
• K. Vaidhya, S. Thirunavukkarasu, V. Alex, and G. Krishnamurthi, “Multi-modal brain tumor segmentation using stacked denoising autoencoders,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9556, pp. 181–194, 2016, doi: 10.1007/978-3-319-30858-6_16.
• H. Su, F. Xing, X. Kong, Y. Xie, S. Zhang, and L. Yang, “Robust cell detection and segmentation in histopathological images using sparse reconstruction and stacked denoising autoencoders,” Advances in Computer Vision and Pattern Recognition, vol. 9351, no. 9783319429984, pp. 257–278, 2017, doi: 10.1007/978-3-319-42999-1_15.
• B. H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, Y. Burren, N. Porz, J. Slotboom, R. Wiest, L. Lanczi, E. Gerstner, M. Weber, T. Arbel, B. B. Avants, N. Ayache, P. Buendia, D. L. Collins, N Cordier, J. J. Corso, A. Criminisi, T. Das, Hervé Delingette, Ç. Demiralp, C. R. Durst, M. Dojat, S. Doyle, J. Festa, F. Forbes, E. Geremia, B. Glocker, P. Golland, X. Guo, Andac Hamamci, Khan M. Iftekharuddin, Raj Jena,Nigel M. John, E. Konukoglu, D. Lashkari, J. A. Mariz, R. Meier, S. Pereira, D. Precup, S. J. Price, T. R. Raviv, S. M. S. Reza, M. Ryan, D. Sarikaya, L. Schwartz, H. C. Shin, J. Shotton, C. A. Silva, N. Sousa, N. K. Subbanna, G. Szekely, T. J. Taylor, O. M. Thomas, N. J. Tustison, G. Unal, F. Vasseur, M. Wintermark, D. H. Ye, L. Zhao, B. Zhao, D. Zikic, M. Prastawa, M. Reyes, and K. V. Leemput, “The multimodal brain tumor image segmentation benchmark (BRATS),” IEEE Transactions on Medical Imaging, vol. 34, no. 10, pp. 1993-2024, 2015, doi: 10.1109/TMI.2014.2377694.
• M. Ahmad, J. Yang, D. Ai, S. F. Qadri, and Y. Wang, “Deep-stacked auto encoder for liver segmentation,” Communications in Computer and Information Science, vol. 757, pp. 243–251, 2018, doi: 10.1007/978-981-10-7389-2_24.
• X. Wang, S. Zhai, and Y. Niu, “Automatic Vertebrae Localization and Identification by Combining Deep SSAE Contextual Features and Structured Regression Forest,” J Digit Imaging, vol. 32, no. 2, pp. 336–348, 2019, doi: 10.1007/S10278-018-0140-5.
• S. Albawi, T. A. Mohammed and S. Al-Zawi, “Understanding of a convolutional neural network,” 2017 International Conference on Engineering and Technology (ICET), Antalya, Turkey, Aug. 21-23, 2017.
• L. Alzubaidi, J. Zhang, A. J. Humaidi, A. Al-Dujaili, Y. Duan, O. Al-Shamma, J. Santamaría, M. A. Fadhel, M. Al-Amidie and L. Farhan “Review of deep learning: concepts, CNN architectures, challenges, applications, future directions,” J Big Data, vol. 8, no. 1, 2021, doi: 10.1186/S40537-021-00444-8.
• Y. Lecun, L. Bottou, Y. Bengio and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, 1998, doi: 10.1109/5.726791.
• M. D. Zeiler and R. Fergus, “Stochastic pooling for regularization of deep convolutional neural networks,” 1st International Conference on Learning Representations, ICLR 2013, arXiv: 1301.3557.
• O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg and L. Fei-Fei “ImageNet Large Scale Visual Recognition Challenge,” Int J Comput Vis, vol. 115, no. 3, pp. 211–252, 2015, doi: 10.1007/S11263-015-0816-Y.
• E. Acar, Ö. Türk, Ö. F. Ertugrul, and E. Aldemır, “Employing deep learning architectures for image-based automatic cataract diagnosis,” Turkish Journal of Electrical Engineering and Computer Sciences, vol. 29, no. 8, pp. 2649–2662, Jan. 2021, doi: 10.3906/elk-2103-77.
• C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed. D. Anguelov, S. Erhan, V. Vanhoucke and A. Rabinovich, “Going deeper with convolutions,” 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, Jun. 07-12, 2015.
• K. He, X. Zhang, S. Ren and J. Sun, “Spatial pyramid pooling in deep convolutional networks for visual recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 9, pp. 1904-1916, 2015, doi: 10.1109/TPAMI.2015.2389824.
• G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, “Densely connected Convolutional networks,” 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, jul. 21-26, 2017.
• H. R. Roth, L. Lu, A. Farag, H. C. Shin, J. Liu, E. B. Turkbey & R. M. Summers “Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation,” Lecture Notes in Computer Science, vol. 9349, pp. 556–564, 2015, doi: 10.1007/978-3-319-24553-9_68.
• S. Hamidian, Sahiner, N. Petrick and A. Pezeshk, “3D convolutional neural network for automatic detection of lung nodules in chest CT,” SPIE Medical Imaging, Orlando, Florida, United States, Mar. 3, 2017
• G. L. França da Silva, T. L. A. Valente, bAristófanes CorrêaSilvaaAnselmo Cardosode PaivaaMarceloGattassb “The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans,” Wiley Online Library, vol. 38, no. 2, pp. 915–931, 2011, doi: 10.1118/1.3528204.
• P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. Lecun, “Overfeat: Integrated recognition, localization and detection using convolutional networks,” arXiv: 1312.6229, 24 Feb. 2014.
• R. Girshick, J. Donahue, T. Darrell and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, Jun. 23-28, 2014.
• F. Ning, D. Delhomme, Y. LeCun, F. Piano, L. Bottou and P.E. Barbano, “Toward automatic phenotyping of developing embryos from videos,” IEEE Transactions on Image Processing, vol. 14, no. 9, pp. 1360-1371, 2005, doi: 10.1109/TIP.2005.852470.
• D. C. Cires¸ancires¸an, A. Giusti, L. M. Gambardella, and J. Schmidhuber, “Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images,” Advances in neural information processing systems, vol. 25, 2012.
• P. Pinheiro and R. Collobert, “Recurrent convolutional neural networks for scene labeling,” Proceedings of the 31st International Conference on Machine Learning, vol. 32, no. 1, pp. 82-90, 2014.
• B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik, “Simultaneous detection and segmentation,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8695 LNCS, no. PART 7, pp. 297–312, 2014, doi: 10.1007/978-3-319-10584-0_20/COVER.
• S. Gupta, R. Girshick, P. Arbeláez, and J. Malik, “Learning rich features from RGB-D images for object detection and segmentation,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8695 LNCS, no. PART 7, pp. 345–360, 2014, doi: 10.1007/978-3-319-10584-0_23/COVER.
• J. Long, E. Shelhamer and T. Darrell, “Fully convolutional networks for semantic segmentation,” 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, Jun. 07-12, 2015.
• O. Ronneberger, P. Fischer and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” In International Conference on Medical image computing and computer-assisted intervention, vol. 9351, pp. 234–241, 2015, doi: 10.1007/978-3-319-24574-4_28.
• A. A. Eker and N. Duru, “Medikal Görüntü İşlemede Derin Öğrenme Uygulamaları,” Acta Infologica, cilt: 5, sayı: 2, s. 459 - 474,2021,doi: 10.26650/acin.927561.
• F. Milletari, N. Navab and S. Ahmadi, “V-net: Fully convolutional neural networks for volumetric medical image segmentation,” 2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA, Oct. 25-28, 2016.
• Y. Lei, S. Tian, X. He, T. Wang, B. Wang, P. Patel, A. B. Jani, H. Mao, W. J. Curran, T. Liu and X. Yang, “Ultrasound prostate segmentation based on multidirectional deeply supervised V‐Net,” Wiley Online Library, vol. 46, no. 7, pp. 3194–3206, 2019, doi: 10.1002/mp.13577.
• B. Wang, Y. Lei, S. Tian, T. Wang, Y. Liu, P. Patel, A. B. Jani, H. Mao, W. J. Curran, T. Liu and X. Yang, “Deeply supervised 3D fully convolutional networks with group dilated convolution for automatic MRI prostate segmentation,” Wiley Online Library, vol. 46, no. 4, pp. 1707–1718, 2019, doi: 10.1002/mp.13416.
• J. Schlemper, O. Oktay, M. Schaap, M. Heinrich, B. Kainz, B. Glocker and D. Rueckert, “Attention gated networks: Learning to leverage salient regions in medical images,” Medical Image Analysis, vol. 53, pp. 197-207, 2019, doi: https://doi.org/10.1016/j.media.2019.01.012.
• J. R. R. Uijlings, K. E. A. van de Sande, T. Gevers, and A. W. M. Smeulders, “Selective search for object recognition,” Int J Comput Vis, vol. 104, no. 2, pp. 154–171, 2013, doi: 10.1007/S11263-013-0620-5.
• R. Girshick, “Fast r-cnn,” 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, Dec. 07-13, 2015
• S. Ren, K. He, R. Girshick and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.39, no. 6, pp. 1137-1149, 2017, doi: 10.1109/TPAMI.2016.2577031.
• P. F. Christ, M. E. Elshaer, F. Ettlinger, S. Tatavarty, M. Bickel, P. Bilic, M. Rempfler, M. Armbruster, F. Hofmann, M. D’Anastasi, W. H. Sommer, S. A. Ahmadi and B. H. Menze, “Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9901 LNCS, pp. 415–423, 2016, doi: 10.1007/978-3-319-46723-8_48.
• I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” Communications of the ACM, vol. 63, no. 11, pp. 139–144, doi: https://doi.org/10.1145/3422622.
• X. Ying, H. Guo, K. Ma, J. Wu, Z. Weng and Y. Zheng, “X2CT-GAN: reconstructing CT from biplanar X-rays with generative adversarial networks,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), CA, USA, Jun. 15-20, 2019.
• X. Dong, Y. Lei, T. Wang, K. Higgins, T. Liu, W. J. Curran, H. Mao, J. Nye and X. Yang, “Deep learning-based attenuation correction in the absence of structural information for whole-body positron emission tomography imaging,” Physics in Medicine & Biology, vol. 65, no. 5, pp. 055011, 2020, doi: https://doi.org/10.1088/1361-6560/ab652c.
• J. Harms, Y. Lei, T. Wang, R. Zhang, J. Zhou, X. Tang, W. J. Curran, T. Liu and X. Yang, “Paired cycle‐GAN‐based image correction for quantitative cone‐beam computed tomography,” Wiley Online Library, vol. 46, no. 9, pp. 3998–4009, 2019, doi: 10.1002/mp.13656.
• X. Dong, Y. Lei, T. Wang, M. Thomas, L. Tang, W. J. Curran, T. Liu and X. Yang, “Automatic multiorgan segmentation in thorax CT images using U‐net‐GAN,” Wiley Online Library, vol. 46, no. 5, pp. 2157–2168, 2019, doi: 10.1002/mp.13458.
• W. Dai, N. Dong, Z. Wang, X. Liang, H. Zhang, and E. P. Xing, “Scan: Structure correcting adversarial network for organ segmentation in chest x-rays,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11045 LNCS, pp. 263–273, 2018, doi: 10.1007/978-3-030-00889-5_30.
• Q. Zhang, H. Wang, H. Lu, D. Won and S. W. Yoon, “Medical image synthesis with generative adversarial networks for tissue recognition,” 2018 IEEE International Conference on Healthcare Informatics (ICHI), New York, NY, USA, Jun. 04-07, 2018.
• M. Frid-Adar, I. Diamant, E. Klang, M. Amitai, J. Goldberger and H. Greenspan, “GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification,” Neurocomputing, vol. 321, pp. 321-331, 2018, doi: https://doi.org/10.1016/j.neucom.2018.09.013.
• G. Shafai-Erfani, T. Wang, Y. Lei, S. Tian, P. Patel, A. B. Jani, W. J. Curran, T. Liu and X. Yang, “Dose evaluation of MRI-based synthetic CT generated using a machine learning method for prostate cancer radiotherapy,” vol. 44, no. 4, pp. e64-e70, 2019
• D. Oszutowska-Mazurek, P. Mazurek, and O. Knap, “Stacked autoencoder for segmentation of bone marrow histological images,” Advances in Intelligent Systems and Computing, vol. 764, pp. 425–435, 2019, doi: 10.1007/978-3-319-91189-2_42/COVER.
• S. Qadri, Z. Zhao, D. Ai, M. Ahmad and Y. Wang, “Vertebrae segmentation via stacked sparse autoencoder from computed tomography images,” Eleventh International Conference on Digital Image Processing (ICDIP 2019), Guangzhou, China, Aug. 14, 2019.
• E. Tappeiner, S. Pröll, M. Hönig, P. F. Raudaschl, P. Zaffino, M. F. Spadea, G. C. Sharp, R. Schubert and K. Fritscher, “Multi-organ segmentation of the head and neck area: an efficient hierarchical neural networks approach,” Int J Comput Assist Radiol Surg, vol. 14, no. 5, pp. 745–754, 2019, doi: 10.1007/S11548-019-01922-4.
• K. Men, X. Chen, Y. Zhang, T. Zhang, J. Dai, J. Yi and Y. Li, “Deep deconvolutional neural network for target segmentation of nasopharyngeal cancer in planning computed tomography images,” Front Oncol, vol. 7, no. DEC, 2017, doi: 10.3389/FONC.2017.00315.
• B. Ibragimov and L. Xing, “Segmentation of organs‐at‐risks in head and neck CT images using convolutional neural networks,” Wiley Online Library, vol. 44, no. 2, pp. 547–557, 2017, doi: 10.1002/mp.12045.
• L. D. van Harten, J. M. H. Noothout, J. J. C. Verhoeff, J. M. Wolterink, and I. Išgum, “Automatic Segmentation of Organs at Risk in Thoracic CT scans by Combining 2D and 3D Convolutional Neural Networks.,” In: SegTHOR@ISBI. in CEUR Workshop Proceedings(2019). vol. 2349, pp. 1-4, 2019, doi: http://ceur-ws.org/Vol-2349/SegTHOR2019_paper_12.pdf
• J. Zhu, J. Zhang, B. Qiu, Y. Liu, X. Liu, and L. Chen, “Comparison of the automatic segmentation of multiple organs at risk in CT images of lung cancer between deep convolutional neural network-based and atlas-based techniques,” Acta Oncol (Madr), vol. 58, no. 2, pp. 257–264, 2019, doi: 10.1080/0284186X.2018.1529421.
• X. Zhou, R. Takayama, S. Wang, T. Hara, and H. Fujita, “Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method,” Wiley Online Library, vol. 44, no. 10, pp. 5221–5233, 2017, doi: 10.1002/mp.12480.
• G. Shi, L. Xiao, Y. Chen and S. K. Zhou, “Marginal loss and exclusion loss for partially supervised multi-organ segmentation,” Medical Image Analysis, vol. 70, pp. 101979, 2021, doi: https://doi.org/10.1016/j.media.2021.101979.
• Y. Zhou, Z. Li, S. Bai, C. Wang, X. Chen, M. Han, E. Fishman and A. L. Yuille, “Prior-aware neural network for partially-supervised multi-organ segmentation,” In Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Korea (South), Oct. 27 – Nov. 02, 2019.
• H. Kim, J. Jung, J. Kim, B. Cho, J. Kwak, J. Y. Jang, S. Lee, J. Lee and S. M. Yoon, “Abdominal multi-organ auto-segmentation using 3D-patch-based deep convolutional neural network,” Scientific reports, vol. 10, no. 1, pp. 1-9, 2020, Art no. 6204, 2020.
• Z. Peng, X. Fang, P. Yan, H. Shan, T. Liu, X. Pei, G. Wang, B. Liu, M. K. Kalra and X. G. Xu, “A method of rapid quantification of patient-specific organ doses for CT using deep-learning-based multi-organ segmentation and GPU-accelerated Monte Carlo dose computing,” Med Phys, vol. 47, no. 6, pp. 2526–2536, 2020, doi: 10.1002/MP.14131.
• J. Cai, Y. Xia, D. Yang, D. Xu, L. Yang, and H. Roth, “End-to-End Adversarial Shape Learning for Abdomen Organ Deep Segmentation,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11861 LNCS, pp. 124–132, 2019, doi: 10.1007/978-3-030-32692-0_15.
• S. Gou, N. Tong, S. Qi, S. Yang, R. Chin and K. Sheng, “Self-channel-and-spatial-attention neural network for automated multi-organ segmentation on head and neck CT images,” Phys. Med. Biol., vol. 65, no. 24, pp. 245034, 2020, doi: https://doi.org/10.1088/1361-6560/ab79c3
• Y. Fu, T. R. Mazur, X. Wu, S. Liu, X. Chang, Y. Lu, H. H. Li, H. Kim, M. C. Roach, L. Henke and D. Yang, “A novel MRI segmentation method using CNN‐based correction network for MRI‐guided adaptive radiotherapy,” Wiley Online Library, vol. 45, no. 11, pp. 5129–5137, 2018, doi: 10.1002/mp.13221.
• Z. Li, Y. Wang, and J. Yu, “Brain tumor segmentation using an adversarial network,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10670 LNCS, pp. 123–132, 2018, doi: 10.1007/978-3-319-75238-9_11.
• D. J. Ho, D. V. K. Yarlagadda, T. M. D’Alfonso, M. G. Hanna, A. Grabenstetter, P. Ntiamoah, E. Brogi, L. K. Tan and T. J. Fuchsab, “Deep multi-magnification networks for multi-class breast cancer image segmentation,” Computerized Medical Imaging and Graphics, vol. 88, pp. 101866, 2021, doi: https://doi.org/10.1016/j.compmedimag.2021.101866
• F. Xing, Y. Xie and L. Yang, “An automatic learning-based framework for robust nucleus segmentation,” IEEE Transactions on Medical Imaging, vol. 35, no. 2, pp. 550-566, 2016, doi: 10.1109/TMI.2015.2481436
• Y. Song, L. Zhang, S. Chen, D. Ni, B. Lei and T. Wang, “Accurate segmentation of cervical cytoplasm and nuclei based on multiscale convolutional network and graph partitioning,” IEEE Transactions on Biomedical Engineering, vol. 62, no. 10, pp. 2421-2433, 2015, doi: 10.1109/TBME.2015.2430895
• L. Houa, V. Nguyen, A. B. Kanevsky, D. Samaras, T. M. Kurc, T. Zhao, R. R. Gupta, Y. Gao, W. Chen, D. Foran and J. H. Salt, “Sparse autoencoder for unsupervised nucleus detection and representation in histopathology images,” Pattern Recognition, vol. 86, pp. 188-200, 2019, doi: https://doi.org/10.1016/j.patcog.2018.09.007
• J. Xu, L. Xiang, Q. Liu, H. Gilmore, J. Wu, J. Tang and A. Madabhushi, "Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images," in IEEE Transactions on Medical Imaging, vol. 35, no. 1, pp. 119-130, 2016, doi: 10.1109/TMI.2015.2458702.
• Y. Song, E. Tan, X. Jiang, J. Cheng, D. Ni, S. Chen, B. Lei and T. Wang, “Accurate cervical cell segmentation from overlapping clumps in pap smear images,” IEEE Transactions on Medical Imaging, vol. 36, no. 1, pp. 288-300, 2017, doi: 10.1109/TMI.2016.2606380
• T. Qaiser, Y. Tsang, D. Taniyama, N. Sakamoto, K. Nakane, D. Epstein and N. Rajpoot, “Fast and accurate tumor segmentation of histology images using persistent homology and deep convolutional features,” Medical Image Analysis, vol. 55, pp. 1-14, 2019, doi: https://doi.org/10.1016/j.media.2019.03.014
• A. Agarwalla, M. Shaban, and N. M. Rajpoot, “Representation-Aggregation Networks for Segmentation of Multi-Gigapixel Histology Images,” arXiv:1707.08814, 27 Jul. 2017.
• S. Graham, H. Chen, J. Gamper, Q. Dou, P. Heng, D. Snead, Y. Tsang and N. Rajpoot “MILD-Net: Minimal information loss dilated network for gland instance segmentation in colon histology images,” Medical Image Analysis, vol. 52, pp. 199-211, 2019, doi: https://doi.org/10.1016/j.media.2018.12.001.
• T. de Bel, M. Hermsen, B. Smeets, L. Hilbrands, J. van der Laak and G. Litjens “Automatic segmentation of histopathological slides of renal tissue using deep learning,” In Medical Imaging 2018: Digital Pathology, Houston, Texas, United States, Mar. 6, 2018.
• Z. Jia, X. Huang, I. Eric, C. Chang and Y. Xu, “Constrained deep weak supervision for histopathology image segmentation,” IEEE Transactions on Medical Imaging, vol. 36, no. 11, pp. 2376-2388, 2017, doi: 10.1109/TMI.2017.2724070
• P. Naylor, M. Laé, F. Reyal and T. Walter, “Segmentation of nuclei in histopathology images by deep regression of the distance map,” IEEE Transactions on Medical Imaging, vol. 38, no. 2, pp. 448-459, 2019, doi: 10.1109/TMI.2018.2865709
• Y. van Eycke, C. Balsat, L. Verset, O. Debeir, I. Salmon and C. Decaestecker, “Segmentation of glandular epithelium in colorectal tumours to automatically compartmentalise IHC biomarker quantification: A deep learning approach,” Medical Image Analysis, vol. 49, pp. 35-45, 2018, doi: https://doi.org/10.1016/j.media.2018.07.004
• F. Gu, N. Burlutskiy, M. Andersson, and L. K. Wilén, “Multi-resolution Networks for Semantic Segmentation in Whole Slide Images,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11039 LNCS, pp. 11–18, 2018, doi: 10.1007/978-3-030-00949-6_2.
• Q. Liang, Y. Nan, G. Coppola, K. Zou, W. Sun, D. Zhang, Y. Wang and G. Yu, “Weakly supervised biomedical image segmentation by reiterative learning,” IEEE Journal of Biomedical and Health Informatics, vol. 23, no. 3, pp. 1205-1214, 2019, doi: 10.1109/JBHI.2018.2850040
• A. BenTaieb and G. Hamarneh, “Topology aware fully convolutional networks for histology gland segmentation,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9901 LNCS, pp. 460–468, 2016, doi: 10.1007/978-3-319-46723-8_53.
• H. Qu, G. Riedlinger, P. Wu, Q. Huang, J. Yi, S. De and D. Metaxas, “Joint segmentation and fine-grained classification of nuclei in histopathology images,” 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), Venice, Italy, Apr. 08-11, 2019.
• S. Graham, Q. D. Vu, S. Raza, A. Azam, Y. Tsang, J. Kwak and N. Rajpoot, “Hover-net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images,” Medical Image Analysis, vol. 58, 2019, doi: https://doi.org/10.1016/j.media.2019.101563
• M. Gadermayr, L. Gupta, V. Appel, P. Boor, B. M. Klinkhammer and D. Merhof “Generative adversarial networks for facilitating stain-independent supervised and unsupervised segmentation: a study on kidney histology,” IEEE Transactions on Medical Imaging, vol. 38, no. 10, pp. 2293-2302, 2019, doi: 10.1109/TMI.2019.2899364
• M. Gadermayr, L. Gupta, B. M. Klinkhammer, P. Boor, and D. Merhof, “Unsupervisedly training GANs for segmenting digital pathology with automatically generated annotations,” arXiv:1805.10059,1 Aug. 2018.
• A. Kapil, T. Wiestler, S. Lanzmich, A. Silva, K. Steele, M. Rebelatto, G. Schmidt and N. Brieu, “DASGAN--Joint Domain Adaptation and Segmentation for the Analysis of Epithelial Regions in Histopathology PD-L1 Images,” arXiv:1906.11118, 26 Jun. 2019.
• B. Xu, J. Liu, X. Hou, B. Liu, J. Garibaldi, L. O. Ellis, A. Green, L. Shen, G. Qiu, “Look, investigate, and classify: a deep hybrid attention method for breast cancer classification,” 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), Venice, Italy, Apr. 8-11, 2019.
• J. M. Bokhorst, H. Pinckaers, P. Van Zwam, I. Nagtegaal, J. Van der Laak, F. Ciompi, “Learning from sparsely annotated data for semantic segmentation in histopathology images,” Proceedings of Machine Learning Research, vol. 102, pp. 84-91, 2019.
• W. Bulten, P. Bandi, J. Hoven, R. van de Loo, J. Lotz, N. Weiss, J. Van der Laak, B. Van Ginneken, C. Hulsbergen- van de Kaa and G. Litjens, “Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standard,” Sci Rep, vol. 9, no. 864, 2019, doi: 10.1038/s41598-018-37257-4
• W. Bulten, H. Pinckaers, H. Pinckaers, H. Van Boven, R. Vink, T. De Bel, B. V. Ginneken, J. Van der Laak, C. Hulsbergen -van de Kaa, and G. Litjens, “Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study,” The Lancet Oncology, vol. 21, no. 2, pp. 233-241, 2020, doi: https://doi.org/10.1016/S1470-2045(19)30739-9
• H. Ding, Z. Pan, Q. Cen, Y. Li, S. Chen, “Multi-scale fully convolutional network for gland segmentation using three-class classification,” Neurocomputing, vol. 380, pp. 150-161, 2020, doi: https://doi.org/10.1016/j.neucom.2019.10.097.
• H. Tokunaga, Y. Teramoto, A. Yoshizawa, and R. Bise, “Adaptive Weighting Multi-Field-Of-View CNN for Semantic Segmentation in Pathology,” 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, Jan. 15-20, 2019.
• H. Pinckaers and G. Litjens, “Neural Ordinary Differential Equations for Semantic Segmentation of Individual Colon Glands,” arXiv:1910.10470, 23 Oct. 2019.
• N. Seth, S. Akbar, S. Nofech-Mozes, S. Salama, A. L. Martel, “Automated segmentation of DCIS in whole slide images,” European Congress on Digital Pathology, vol. 11435, pp. 67–74, 2019, doi: 10.1007/978-3-030-23937-4_8.
• Y. Liu, Y. Lei, Y. Fu, T. Wang, J. Zhou, X. Jiang, M. McDonald, J. J. Beitler, W. J. Curran, T. Liu, and X. Yanga, “Head and neck multi‐organ auto‐segmentation on CT images aided by synthetic MRI,” Wiley Online Library, vol. 47, no. 9, pp. 4294–4302, 2020, doi: 10.1002/mp.14378.
• P. Hu, F. Wu, J. Peng, Y. Bao, F. Chen, and D. Kong, “Automatic abdominal multi-organ segmentation using deep convolutional neural network and time-implicit level sets,” Int J Comput Assist Radiol Surg, vol. 12, no. 3, pp. 399–411, 2017, doi: 10.1007/S11548-016-1501-5.
• E. Gibson, F. Giganti, Y. Hu, E. Bonmati, S. Bandula, K. Gurusamy, B. Davidson, Stephen P. Perira, M. J. Clarkson and D. C. Barratt, “Automatic multi-organ segmentation on abdominal CT with dense V-networks,” IEEE Transactions on Medical Imaging, vol. 38, no. 8, pp. 1822-1834, 2018, doi: 10.1109/TMI.2018.2806309.
• S. Chen, X. Zhong, S. Hu, S. Dorn, M. Kachelrieb, M. Lell and A. Maier, “Automatic multi-organ segmentation in dual-energy CT (DECT) with dedicated 3D fully convolutional DECT networks,” Med Phys, vol. 47, no. 2, pp. 552–562, 2020, doi: 10.1002/MP.13950.
• M. N. Gurcan, L. E. Boucheron, A. Can, A. Madabhushi, N. M. Rajpoot and B. Yener, “Histopathological image analysis: A review,” IEEE Reviews in Biomedical Engineering, vol.2, pp. 147-171, 2009, doi: 10.1109/RBME.2009.2034865
• A. Madabhushi and G. Lee, “Image analysis and machine learning in digital pathology: Challenges and opportunities,” Medical Image Analysis, vol. 33, pp. 170-175, 2016, doi: https://doi.org/10.1016/j.media.2016.06.037
• A. H. Beck, A. R. Sangoı, S. Leung, R. J. Marinelli, T. O. Nielsen, M. J. Van De Vıjver, R. B. West, M. Van De Rıjn and D. Koller, “Systematic Analysis of Breast Cancer Morphology Uncovers Stromal Features Associated with Survival,” Science Translational Medicine, vol. 3, no. 108, pp.108-113, 2011, doi: 10.1126/scitranslmed.3002564.
• K. Bera, K. A. Schalper, D. L. Rimm, V. Velcheti, and A. Madabhushi, “Artificial intelligence in digital pathology-new tools for diagnosis and precision oncology,” Nature Reviews Clinical Oncology, vol. 16, no. 11, pp. 703–715, 2019, doi: 10.1038/s41571-019-0252-y.
• D. C. Cireşan, A. Giusti, L. M. Gambardella, and J. Schmidhuber, “Mitosis detection in breast cancer histology images with deep neural networks,” In International conference on medical image computing and computer-assisted intervention, Springer, Berlin, Heidelberg, Sep. 411-418, 2013.