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Review

A review of medical image detection for cancers in digestive system based on artificial intelligence

, , , &
Pages 877-889 | Received 02 Jul 2019, Accepted 13 Sep 2019, Published online: 30 Sep 2019

References

  • Ferlay J, Colombet M, Soerjomataram I, et al. Global and regional estimates of the incidence and mortality for 38 cancers: GLOBOCAN 2018. Lyon: International Agency for Research on Cancer/World Health Organization; 2018.
  • Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin. 2018;68(6):394–424.
  • Siegel RL, Miller KD, Dvm AJ. Cancer statistics, 2017. Ca A Cancer J Clinicians. 2017;67(1):7–30.
  • Pourhoseingholi MA, Vahedi M, Baghestani AR. Burden of gastrointestinal cancer in Asia; an overview. Gastroenterol Hepatol Bed Bench. 2015;8(1):19–27.
  • Xie FY, Xu WH, Yin C, et al. Nanomedicine strategies for sustained, controlled, and targeted treatment of cancer stem cells of the digestive system. World J Gastrointest Oncol. 2016;8(10):735–744.
  • Wu L, Qu X. Cancer biomarker detection: recent achievements and challenges. Chem Soc Rev. 2015;44(10):2963–2997.
  • Cover TM, Hart PE. Nearest neighbor pattern classification. IEEE Trans Inf Theory. 1967;13(1):21–27.
  • Rumelhart DE. Learning representations by back-propagating errors. Nature. 1986;323:533–536.
  • Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;273–297.
  • Kilic N, Kursun O, Ucan ON. Classification of the colonic polyps in ct-colonography using region covariance as descriptor features of suspicious regions. J Med Syst. 2010;34(2):101–105.
  • Reddy DJ, Prasath TA, Rajasekaran MP, et al. Brain and pancreatic tumor classification based on GLCM—k-NN approaches. International Conference on Intelligent Computing and Applications. Springer (Singapore); 2019.p. 293–302.
  • Săftoiu A, Vilmann P, Gorunescu F, et al. Efficacy of an artificial neural network–based approach to endoscopic ultrasound elastography in diagnosis of focal pancreatic masses. Clin Gastroenterol Hepatol. 2012;10(1):84–90.
  • Zhang MM, Yang H, Jin ZD, et al. Differential diagnosis of pancreatic cancer from normal tissue with digital imaging processing and pattern recognition based on a support vector machine of EUS images. Gastrointest Endosc. 2010;72(5):978–985.
  • Gatos I, Tsantis S, Spiliopoulos S, et al. A new automated quantification algorithm for the detection and evaluation of focal liver lesions with contrast‐enhanced ultrasound. Med Phys. 2015;42(7):3948–3959.
  • Ulagamuthalvi V, Sridharan D. Automatic identification of ultrasound liver cancer tumor using support vector machine. In Proceedings of the International Conference on Emerging Trends in Computer and Electronics Engineering, Dubai; 2012. p. 41–43.
  • Kondo S, Takagi K, Nishida M, et al. Computer-aided diagnosis of focal liver lesions using contrast-enhanced ultrasonography with perflubutane microbubbles. IEEE Trans Med Imaging. 2017;36(7):1427–1437.
  • Li S, Jiang H, Wang Z, et al. An effective computer aided diagnosis model for pancreas cancer on PET/CT images. Comput Methods Programs Biomed. 2018;165:205–214.
  • Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst. 2012;1:1097–1105.
  • Long J, Shelhamer E, Darrell T Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition; IEEE, Boston, MA, USA. 2015. p. 3431–3440.
  • Ronneberger O, Fischer P, Brox T U-net: convolutional networks for biomedical image segmentation. International Conference on Medical image computing and computer-assisted intervention. Springer (Cham). 2015; 234–241.
  • Lecun YL, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition. Proc IEEE. 1998;86(11):2278–2324.
  • Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. In Proceedings of the International Conference on Learning Representations (ICLR). San Diego, CA, arXiv Preprint arXiv:14091556. 2014, p. 1–14.
  • Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR). IEEE, Boston, MA, USA; 2015. p. 1–9.
  • He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR). IEEE, Las Vegas, NV, USA. 2016; 770–778.
  • Sharma H, Zerbe N, Klempert I, et al. Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology. Computerized Med Imaging Graphics. 2017;61:2–13.
  • Shin Y, Qadir HA, Aabakken L, et al. Automatic colon polyp detection using region based deep CNN and post learning approaches. IEEE Access. 2018;6:40950–40962.
  • Sun C, Guo S, Zhang H, et al. Automatic segmentation of liver tumors from multiphase contrast-enhanced CT images based on FCNs. Artif Intell Med. 2017;83:58–66.
  • Christ PF, Elshaer MEA, Ettlinger F, et al. Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields. International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer (Cham); 2016. p. 415–423.
  • Christ PF, Ettlinger F, Grün F, et al. Automatic liver and tumor segmentation of ct and mri volumes using cascaded fully convolutional neural networks. arXiv Preprint arXiv:1702.05970. 2017. p. 1–20.
  • Ben-Cohen A, Diamant I, Klang E, et al. Fully convolutional network for liver segmentation and lesions detection. Deep Learning and Data Labeling for Medical Applications. Springer (Cham); 2016. p. 77–85.
  • Vorontsov E, Tang A, Pal C, et al. Liver lesion segmentation informed by joint liver segmentation. In Proceedings of the 15th International Symposium on Biomedical Imaging (ISBI 2018). IEEE, Washington, DC, USA, 2018. p. 1332–1335.
  • Zhou Y, Xie L, Fishman EK, et al. Deep supervision for pancreatic cyst segmentation in abdominal CT scans. International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer (Cham); 2017. p. 222–230.
  • Li Y, Xie X, Liu S, et al. GT-Net: a deep learning network for gastric tumor diagnosis. 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, Volos, Greece; 2018. p. 20–24.
  • Liang Q, Nan Y, Coppola G, et al. Weakly-supervised biomedical image segmentation by reiterative learning. IEEE J Biomed Health Inform. 2018;23(3):1205–1214.
  • Fechter T, Adebahr S, Baltas D, et al. Esophagus segmentation in CT via 3D fully convolutional neural network and random walk. Med Phys. 2017;44(12):6341–6352.
  • Hao Z, Liu J, Liu J Esophagus tumor segmentation using fully convolutional neural network and graph cut. Chinese Intelligent Systems Conference. Springer (Singapore); 2017. p. 413–420.
  • Xue DX, Zhang R, Zhao YY, et al. Fully convolutional networks with double-label for esophageal cancer image segmentation by self-transfer learning. Ninth Int Conf Digital Image Process (ICDIP 2017). Int Soc Opt Photonics; 2017. 10420:104202D.
  • Roth H, Oda M, Shimizu N, et al. Towards dense volumetric pancreas segmentation in CT using 3D fully convolutional networks. Med Imaging 2018: Image Process. Int Soc Opt Photonics; 2018;10574:105740B.
  • Soomro MH, De Cola G, Conforto S, et al. Automatic segmentation of colorectal cancer in 3D MRI by combining deep learning and 3D level-set algorithm-a preliminary study. 2018 IEEE 4th Middle East Conference on Biomedical Engineering (MECBME). IEEE, Tunis, Tunisia; 2018. p. 198–203.
  • Chlebus G, Schenk A, Moltz JH, et al. Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing. Sci Rep. 2018;8(1):15497.
  • Quo Z, Zhang L, Lu L, et al. Deep LOGISMOS: deep learning graph-based 3D segmentation of pancreatic tumors on CT scans. In Proceedings of the 15th International Symposium on Biomedical Imaging (ISBI 2018). IEEE, Washington, DC, USA; 2018. p. 1230–1233.
  • Yousefi S, Sokooti H, Elmahdy MS, et al. Esophageal gross tumor volume segmentation using a 3D convolutional neural network. International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer (Cham); 2018. p. 343–351.
  • Yu Z, Yang S, Zhou K, et al. A low computational approach for assistive esophageal adenocarcinoma and colorectal cancer detection. UK Workshop on Computational Intelligence. Springer (Cham); 2018. p. 169–178.
  • Ebigbo A, Mendel R, Probst A, et al. Computer-aided diagnosis using deep learning in the evaluation of early oesophageal adenocarcinoma. Gut. 2019;68(7):1143–1145.
  • Takiyama H, Ozawa T, Ishihara S, et al. Automatic anatomical classification of esophagogastroduodenoscopy images using deep convolutional neural networks. Sci Rep. 2018;8(1):7497.
  • Garcia-Peraza-Herrera LC, Everson M, Li W, et al. Interpretable fully convolutional classification of intrapapillary capillary loops for real-time detection of early squamous neoplasia. arXiv Preprint arXiv:1805.00632. 2018. p. 1–8.
  • Horie Y, Yoshio T, Aoyama K, et al. Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks. Gastrointest Endosc. 2019;89(1):25–32.
  • Van Riel S, Van Der Sommen F, Zinger S, et al. Automatic detection of early esophageal cancer with CNNS using transfer learning. In Proceedings of the IEEE International Conference on Image Processing (ICIP). IEEE, Athens, Greece; 2018. p. 1383–1387.
  • Hong J, Park B, Park H Convolutional neural network classifier for distinguishing Barrett’s esophagus and neoplasia endomicroscopy images. In Proceedings of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, Seogwipo, South Korea; 2017. p. 2892–2895.
  • Qin P, Chen J, Zeng J, et al. Large-scale tissue histopathology image segmentation based on feature pyramid. EURASIP J Image Video Process. 2018;2018(1):75.
  • Billah M, Waheed S, Rahman MM. An automatic gastrointestinal polyp detection system in video endoscopy using fusion of color wavelet and convolutional neural network features. Int J Biomed Imaging. 2017;2017:1–9.
  • Zhu Y, Wang QC, Xu MD, et al. Application of convolutional neural network in the diagnosis of the invasion depth of gastric cancer based on conventional endoscopy. Gastrointest Endosc. 2019;89(4):806-815.e1.
  • Li Y, Li X, Xie X, et al. Deep learning based gastric cancer identification. In Proceedings of the 15th International Symposium on Biomedical Imaging (ISBI 2018). IEEE, Washington, DC, USA, 2018. p. 182–185.
  • Pogorelov K, Riegler M, Eskeland SL, et al. Efficient disease detection in gastrointestinal videos–global features versus neural networks. Multimed Tools Appl. 2017;76(21):22493–22525.
  • Ishioka M, Hirasawa T, Tada T. Detecting gastric cancer from video images using convolutional neural networks. Digestive Endosc. 2019;31(2):e34–e35.
  • Zhu R, Zhang R, Xue D Lesion detection of endoscopy images based on convolutional neural network features. In Proceedings of the 8th International Congress on Image and Signal Processing (CISP). IEEE, Shenyang, China, 2015. p. 372–376.
  • Georgakopoulos SV, Iakovidis DK, Vasilakakis M, et al. Weakly-supervised convolutional learning for detection of inflammatory gastrointestinal lesions. In Proceedings of the International Conference on Imaging Systems and Techniques (IST). IEEE, Chania, Greece, 2016; 510–514.
  • Shou J, Li Y, Yu G, et al. Whole Slide Image Classification of Gastric Cancer using Convolutional Neural Networks. 2018. Available from: https://openreview.net/forum?id=rkZOp9joz¬eId=rkZOp9joz
  • Sakai Y, Takemoto S, Hori K, et al. Automatic detection of early gastric cancer in endoscopic images using a transferring convolutional neural network. 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2018; 4138–4141.
  • Liu X, Wang C, Hu Y, et al. Transfer learning with convolutional neural network for early gastric cancer classification on magnifiying narrow-band imaging images. In Proceedings of the 25th IEEE International Conference on Image Processing (ICIP). IEEE, Athens, Greece; 2018. p. 1388–1392.
  • Hirasawa T, Aoyama K, Tanimoto T, et al. Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images. Gastric Cancer. 2018;21(4):653–660.
  • Sun JY, Lee SW, Kang MC, et al. A novel gastric ulcer differentiation system using convolutional neural networks. In Proceedings of the 31st International Symposium on Computer-Based Medical Systems (CBMS). IEEE, Karlstad, Sweden; 2018. p. 351–356.
  • Zhang X, Hu W, Chen F, et al. Gastric precancerous diseases classification using CNN with a concise model. PloS One. 2017;12(9):e0185508.
  • Yang X, Ye X, Slabaugh G. Multilabel region classification and semantic linking for colon segmentation in CT colonography. IEEE Trans Biomed Eng. 2015;62(3):948–959.
  • Ren Y, Ma J, Xiong J, et al. Improved false positive reduction by novel morphological features for computer-aided polyp detection in CT colonography. IEEE J Biomed Health Inform. 2019;23(1):324–333.
  • Zhang R, Zheng Y, Poon CCY, et al. Polyp detection during colonoscopy using a regression-based convolutional neural network with a tracker. Pattern Recogn. 2018;83:209–219.
  • Komeda Y, Handa H, Watanabe T, et al. Computer-aided diagnosis based on convolutional neural network system for colorectal polyp classification: preliminary experience. Oncology. 2017;93(Suppl. 1):30–34.
  • Trebeschi S, van Griethuysen JJM, Lambregts DMJ, et al. Deep learning for fully-automated localization and segmentation of rectal cancer on multiparametric MR. Sci Rep. 2017;7(1):5301.
  • Qaiser T, Tsang YW, Taniyama D, et al. Fast and accurate tumor segmentation of histology images using persistent homology and deep convolutional features. Med Image Anal. 2019;55:1–14.
  • Men K, Boimel P, Janopaul-Naylor J, et al. Cascaded atrous convolution and spatial pyramid pooling for more accurate tumor target segmentation for rectal cancer radiotherapy. Phys Med Biol. 2018;63(18):185016.
  • Wang J, Lu J, Qin G, et al. A deep learning‐based autosegmentation of rectal tumors in MR images. Med Phys. 2018;45(6):2560–2564.
  • Chen Y, Ren Y, Fu L, et al. A 3D convolutional neural network framework for polyp candidates detection on the limited dataset of CT colonography. In Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, Honolulu, HI, USA; 2018. p. 678–681.
  • Liu J, Wang D, Lu L, et al. Detection and diagnosis of colitis on computed tomography using deep convolutional neural networks. Med Phys. 2017;44(9):4630–4642.
  • Sirinukunwattana K, Raza SEA, Tsang YW, et al. Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans Med Imaging. 2016;35(5):1196–1206.
  • Mohammed A, Yildirim S, Farup I, et al. Y-Net: a deep convolutional neural network for polyp detection. arXiv Preprint arXiv:180601907. 2018.p. 1–11.
  • Ribeiro E, Uhl A, Häfner M. Colonic polyp classification with convolutional neural networksIn Proceedings of the 29th International Symposium on Computer-Based Medical Systems (CBMS). IEEE, Dublin, Ireland; 2016. p. 253–258.
  • Vivanti R, Ephrat A, Joskowicz L, et al. Automatic liver tumor segmentation in follow up CT studies using convolutional neural networks. In Proceedings of the Patch-Based Methods in Medical Image Processing Workshop, MICCAI, Munich, Germany; 2015. p. 2.
  • Bellver M, Maninis KK, Pont-Tuset J, et al. Detection-aided liver lesion segmentation using deep learning. In Proceedings of the Machine Learning 4 Health Workshop (NIPS 2017), Long Beach, CA, USA; arXiv Preprint arXiv:171111069. 2017. p.1–5.
  • Yuan Y. Hierarchical convolutional-deconvolutional neural networks for automatic liver and tumor segmentation. arXiv Preprint arXiv:171004540. 2017. p. 1–4.
  • Hoogi A, Lambert JW, Zheng Y, et al. A fully-automated pipeline for detection and segmentation of liver lesions and pathological lymph nodes. arXiv Preprint arXiv:170306418. 2017. p. 1–4.
  • Todoroki Y, Han XH, Iwamoto Y, et al. Detection of liver tumor candidates from CT images using deep convolutional neural networks. International Conference on Innovation in Medicine and Healthcare. Springer, Cham. 2017; 140–145.
  • Ben-Cohen A, Klang E, Kerpel A, et al. Fully convolutional network and sparsity-based dictionary learning for liver lesion detection in CT examinations. Neurocomputing. 2018;275:1585–1594.
  • Ben-Cohen A, Klang E, Raskin SP, et al. Cross-modality synthesis from CT to PET using FCN and GAN networks for improved automated lesion detection. Eng Appl Artif Intell. 2019;78:186–194.
  • Wu K, Chen X, Ding M. Deep learning based classification of focal liver lesions with contrast-enhanced ultrasound. Optik Int J Light Electron Opt. 2014;125(15):4057–4063.
  • Meng D, Zhang L, Cao G, et al. Liver fibrosis classification based on transfer learning and FCNet for ultrasound images. Ieee Access. 2017;5:5804–5810.
  • Kumar SS, Moni RS, Rajeesh J. An automatic computer-aided diagnosis system for liver tumors on computed tomography images. Comput Electr Eng. 2013;39(5):1516–1526.
  • Wang Q, Que D. Staging of hepatocellular carcinoma using deep feature in contrast-enhanced MR images. Adv Com Sci Res. 2017;74:198–201.
  • Li W, Jia F, Hu Q. Automatic segmentation of liver tumor in CT images with deep convolutional neural networks. J Comput Commun. 2015;3(11):146.
  • Hoogi A, Subramaniam A, Veerapaneni R, et al. Adaptive estimation of active contour parameters using convolutional neural networks and texture analysis. IEEE Trans Med Imaging. 2017;36(3):781–791.
  • Chlebus G, Meine H, Moltz JH, et al. Neural network-based automatic liver tumor segmentation with random forest-based candidate filtering. arXiv Preprint arXiv:170600842. 2017. p. 1–4.
  • Li X, Chen H, Qi X, et al. H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes. IEEE Trans Med Imaging. 2018;37:2663–2674.
  • Oktay O, Schlemper J, Folgoc LL, et al. Attention U-Net: learning where to look for the pancreas. In the Proceedings of the 1st Conference on Medical Imaging with Deep Learning (MIDL 2018), Amsterdam, The Netherlands; arXiv Preprint arXiv:180403999. 2018. p. 1–10.
  • Fu M, Wu W, Hong X, et al. Hierarchical combinatorial deep learning architecture for pancreas segmentation of medical computed tomography cancer images. BMC Syst Biol. 2018;12(4):56.
  • Zhu Z, Xia Y, Xie L, et al. Multi-scale coarse-to-fine segmentation for screening pancreatic ductal adenocarcinoma. arXiv Preprint arXiv:180702941. 2018. p. 1–9.
  • Momeni‐Boroujeni A, Yousefi E, Somma J. Computer‐assisted cytologic diagnosis in pancreatic Fna: an application of neural networks to image analysis. Cancer Cytopathol. 2017;125(12):926–933.
  • Li H, Lin K, Reichert M, et al. Differential diagnosis for pancreatic cysts in CT scans using densely-connected convolutional networks. arXiv Preprint arXiv:180601023. 2018. p. 1–9.
  • Liu F, Xie L, Xia Y, et al. Joint shape representation and classification for detecting PDAC. arXiv Preprint arXiv:180410684. 2018. p. 1–8.
  • Chang YH, Thibault G, Madin O, et al. Deep learning based nucleus classification in pancreas histological images. Engineering in Medicine and Biology Society (EMBC), 2017 39th Annual International Conference of the IEEE. IEEE, Seogwipo, South Korea; 2017. p. 672–675.
  • Farag A, Lu L, Roth HR, et al. A bottom-up approach for pancreas segmentation using cascaded superpixels and (deep) image patch labeling. IEEE Trans Image Process. 2017;26(1):386–399.
  • Roth HR, Lu L, Farag A, et al. Deeporgan: multi-level deep convolutional networks for automated pancreas segmentation. International conference on medical image computing and computer-assisted intervention. Springer (Cham); 2015. p. 556–564.
  • Heinrich MP, Oktay O BRIEFnet: deep pancreas segmentation using binary sparse convolutions. International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer (Cham); 2017. p. 329–337.
  • Dmitriev K, Kaufman AE, Javed AA, et al. Classification of pancreatic cysts in computed tomography images using a random forest and convolutional neural network ensemble. International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer (Cham); 2017. p. 150–158.
  • Niazi MKK, Tavolara TE, Arole V, et al. Identifying tumor in pancreatic neuroendocrine neoplasms from Ki67 images using transfer learning. PloS One. 2018;13(4):e0195621.

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