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A deep learning based review on abdominal images

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Abstract

Computer-aided diagnosis have stumbled rapidly in the last few years. One of foremost step in computer-aided diagnosis is organ classification and segmentation. Among various organ segmentation techniques, the segmentation of abdominal organs like liver, stomach, kidney, pancreas and bladder from different modality of images has gotten keen interest in past few years. Mostly the interpretations of abdominal images are being done by medical experts or radiologists. Image interpretation by human experts is quite limited due to its subjectivity, complexity of the image, extensive variations exist across different interpreters, and fatigue. After the success of deep learning in real world applications, it is also providing exciting solutions with good accuracy for medical imaging and is seen as a key method for future applications in medical field. Emergence of deep Convolutional Neural Networks (CNN) tends to provide better classification in abdominal imaging analysis as compared to traditional models. This paper presents the state of the art of abdominal images for classifying abdominal organs based on deep learning and is a useful for computer-aided diagnosis applications. First this paper describe background of abdominal organs as well as modalities of imaging system. Then, we reviewed the techniques of deep learning for image segmentation, object detection, classification and other related tasks for multiorgan and single organ abdominal images. For single organ, different organs of abdomen such as liver, kidney, pancreas, and stomach are discussed seprately. In the last section, we have discussed current market challenges and the future recommendations.

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References

  1. VanGinneken B, Schaefer-Prokop CM, Prokop M (2011) Computer-aided diagnosis: how to move from the laboratory to the clinic. Radiology 261 (3):719–732

    Article  Google Scholar 

  2. Sykes J (2014) Reflections on the current status of commercial automated segmentation systems in clinical practice. Journal of medical radiation sciences 61(3):131–134

    Article  MathSciNet  Google Scholar 

  3. Bengio Y, Courville AC, Vincent P (2012) Unsupervised feature learning and deep learning: A review and new perspectives. CoRR, abs/1206.5538 1:2012

    Google Scholar 

  4. Gross RE (1948) A new method for surgical treatment of large omphaloceles. Surgery 24(2):277–292

    Google Scholar 

  5. Kron IL, Harman PKENT, Nolan STANTONP (1984) The measurement of intra-abdominal pressure as a criterion for abdominal re-exploration. Annals of surgery 199(1):28

    Article  Google Scholar 

  6. Malbrain ManuLNG, Cheatham ML, Kirkpatrick A, Sugrue M, Parr M, DeWaele J, Balogh Z, Leppäniemi A, Olvera C, Ivatury R, et al. (2006) Results from the international conference of experts on intra-abdominal hypertension and abdominal compartment syndrome. i. definitions. Intensive care medicine 32(11):1722–1732

    Article  Google Scholar 

  7. Cheatham ML, Malbrain ManuLNG, Kirkpatrick A, Sugrue M, Parr M, DeWaele J, Balogh Z, Leppäniemi A, Olvera C, Ivatury R, et al. (2007) Results from the international conference of experts on intra-abdominal hypertension and abdominal compartment syndrome. ii. recommendations. Intensive care medicine 33(6):951–962

    Article  Google Scholar 

  8. Kirkpatrick AW, Roberts DJ, DeWaele J, Jaeschke R, Malbrain ManuLNG, DeKeulenaer B, Duchesne J, Bjorck M, Leppaniemi A, Ejike JC, et al. (2013) Intra-abdominal hypertension and the abdominal compartment syndrome: updated consensus definitions and clinical practice guidelines from the world society of the abdominal compartment syndrome. Intensive care medicine 39(7):1190–1206

    Article  Google Scholar 

  9. Liu CN, Fatemi M, Waag RC (1983) Digital processing for improvement of ultrasonic abdominal images. IEEE transactions on medical imaging 2 (2):66–75

    Article  Google Scholar 

  10. Mharib AM, Ramli AR, Mashohor S, Mahmood RB (2012) Survey on liver ct image segmentation methods. Artif Intell Rev 37(2):83–95

    Article  Google Scholar 

  11. Priyadarsini S, Selvathi D (2012) Survey on segmentation of liver from ct images. In: 2012 IEEE international conference on advanced communication control and computing technologies (ICACCCT), IEEE, pp 234–238

  12. Campadelli P, Casiraghi E, Esposito A (2009) Liver segmentation from computed tomography scans: a survey and a new algorithm. Artificial intelligence in medicine 45(2-3):185–196

    Article  Google Scholar 

  13. Sindhuja D, Priyadarsini RJ (2016) A survey on classification techniques in data mining for analyzing liver disease disorder. International Journal of Computer Science and Mobile Computing 5(5):483–488

    Google Scholar 

  14. Kumar MK, Sreedevi M, Reddy YCAP (2018) Survey on machine learning algorithms for liver disease diagnosis and prediction. International Journal of Engineering and Technology (UAE) 7:99–102

    Article  Google Scholar 

  15. Kefelegn S, Kamat P (2018) Prediction and analysis of liver disorder diseases by using data mining technique: survey. International Journal of Pure and Applied Mathematics 118(9):765–770

    Google Scholar 

  16. Singh A, Pandey B (2014) Intelligent techniques and applications in liver disorders: a survey. Int J Biomed Eng Technol 16(1):27–70

    Article  Google Scholar 

  17. Huang Q, Zhang F, Li X (2018) Machine learning in ultrasound computer-aided diagnostic systems: a survey. BioMed research international, 2018

  18. Wolz R, Chu C, Misawa K, Fujiwara M, Mori K, Rueckert D (2013) Automated abdominal multi-organ segmentation with subject-specific atlas generation. IEEE transactions on medical imaging 32(9):1723–1730

    Article  Google Scholar 

  19. Chu C, Oda M, Kitasaka T, Misawa K, Fujiwara M, Hayashi Y, Nimura Y, Rueckert D, Mori K (2013) Multi-organ segmentation based on spatially-divided probabilistic atlas from 3d abdominal ct images. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 165–172

  20. Iglesias JE, Sabuncu MR (2015) Multi-atlas segmentation of biomedical images: a survey. Medical image analysis 24(1):205–219

    Article  Google Scholar 

  21. Cerrolaza JJ, Reyes M, Summers RM, González-Ballester MA, Linguraru MG (2015) Automatic multi-resolution shape modeling of multi-organ structures. Medical image analysis 25(1):11–21

    Article  Google Scholar 

  22. Okada T, Linguraru MG, Hori M, Summers RM, Tomiyama N, Sato Y (2015) Abdominal multi-organ segmentation from ct images using conditional shape–location and unsupervised intensity priors. Medical image analysis 26(1):1–18

    Article  Google Scholar 

  23. Wang Z, Bhatia KK, Glocker B, Marvao A, Dawes T, Misawa K, Mori K, Rueckert D (2014) Geodesic patch-based segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 666–673

  24. Xu Z, Burke RP, Lee CP, Baucom RB, Poulose BK, Abramson RG, Landman BA (2015) Efficient multi-atlas abdominal segmentation on clinically acquired ct with simple context learning. Medical image analysis 24 (1):18–27

    Article  Google Scholar 

  25. Tong T, Wolz R, Wang Z, Gao Q, Misawa K, Fujiwara M, Mori K, Hajnal JV, Rueckert D (2015) Discriminative dictionary learning for abdominal multi-organ segmentation. Medical image analysis 23(1):92–104

    Article  Google Scholar 

  26. Suzuki M, Linguraru MG, Okada K (2012) Multi-organ segmentation with missing organs in abdominal ct images. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 418–425

  27. Shimizu A, Ohno R, Ikegami T, Kobatake H, Nawano S, Smutek D (2007) Segmentation of multiple organs in non-contrast 3d abdominal ct images. International journal of computer assisted radiology and surgery 2(3-4):135–142

    Article  Google Scholar 

  28. Park H, Bland PH, Meyer CR (2003) Construction of an abdominal probabilistic atlas and its application in segmentation. IEEE Transactions on medical imaging 22(4):483–492

    Article  Google Scholar 

  29. Campadelli P, Casiraghi E, Pratissoli S, Lombardi G (2009) Automatic abdominal organ segmentation from ct images. ELCVIA: electronic letters on computer vision and image analysis 8(1):1–14

    Article  Google Scholar 

  30. Saxena S, Sharma N, Sharma S, Singh SK, Verma A (2016) An automated system for atlas based multiple organ segmentation of abdominal ct images. Journal of Advances in Mathematics and Computer Science 12(1):1–14

    Google Scholar 

  31. He B, Huang C, Jia F (2015) Fully automatic multi-organ segmentation based on multi-boost learning and statistical shape model search.. In: VISCERAL Challenge@ ISBI, pp 18–21

  32. Lombaert H, Zikic D, Criminisi A, Ayache N (2014) Laplacian forests: Semantic image segmentation by guided bagging. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 496–504

  33. Gibson E, Giganti F, Hu Y, Bonmati E, Bandula S, Gurusamy K, Davidson B, Pereira SP, Clarkson MJ, Barratt DC (2018) Automatic multi-organ segmentation on abdominal ct with dense v-networks. IEEE transactions on medical imaging 37(8):1822–1834

    Article  Google Scholar 

  34. Roth HR, Lu L, Farag A, Shin H-C, Liu J, Turkbey EB, Summers RM (2015) Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation. In: International conference on medical image computing and computer-assisted intervention, Springer, pp 556–564

  35. Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, et al. (2013) The cancer imaging archive (tcia): maintaining and operating a public information repository. Journal of digital imaging 26(6):1045–1057

    Article  Google Scholar 

  36. Xu Z, Lee CP, Heinrich MP, Modat M, Rueckert D, Ourselin S, Abramson RG, Landman BA (2016) Evaluation of six registration methods for the human abdomen on clinically acquired ct. IEEE Trans Biomed Eng 63 (8):1563–1572

    Article  Google Scholar 

  37. Zhou Y, Wang Y, Tang P, Bai S, Shen W, Fishman E, Yuille A (2019) Semi-supervised 3d abdominal multi-organ segmentation via deep multi-planar co-training. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), IEEE, pp 121–140

  38. Zhou X, Ito T, Takayama R, Wang S, Hara T, Fujita H (2016) Three-dimensional ct image segmentation by combining 2d fully convolutional network with 3d majority voting. In: Deep Learning and Data Labeling for Medical Applications. Springer, pp 111–120

  39. González G, Washko GR, Estépar R SJ (2018) Multi-structure segmentation from partially labeled datasets. application to body composition measurements on ct scans. In: Image Analysis for Moving Organ, Breast, and Thoracic Images. Springer, pp 215–224

  40. Regan EA, Hokanson JE, Murphy JR, Make B, Lynch DA, Beaty TH, Curran-Everett D, Silverman EK, Crapo JD (2011) Genetic epidemiology of copd (copdgene) study design. COPD: Journal of Chronic Obstructive Pulmonary Disease 7(1):32–43

    Article  Google Scholar 

  41. Cheng PM, Malhi HS (2017) Transfer learning with convolutional neural networks for classification of abdominal ultrasound images. Journal of digital imaging 30(2):234–243

    Article  Google Scholar 

  42. Roth HR, Oda H, Hayashi Y, Oda M, Shimizu N, Fujiwara M, Misawa K, Mori K (2017) Hierarchical 3d fully convolutional networks for multi-organ segmentation. arXiv preprint arXiv:1704.06382

  43. Larsson M, Zhang Y, Kahl F (2018) Robust abdominal organ segmentation using regional convolutional neural networks. Appl Soft Comput 70:465–471

    Article  Google Scholar 

  44. Gruber N, Antholzer S, Jaschke W, Kremser C, Haltmeier M (2019) A joint deep learning approach for automated liver and tumor segmentation. arXiv preprint arXiv:1902.07971

  45. Frid-Adar M, Diamant I, Klang E, Amitai M, Goldberger J, Greenspan H (2018) Gan-based synthetic medical image augmentation for increased cnn performance in liver lesion classification. Neurocomputing 321:321–331

    Article  Google Scholar 

  46. Li W, Jia F, Hu Q (2015) Automatic segmentation of liver tumor in ct images with deep convolutional neural networks. Journal of Computer and Communications 3(11):146

    Article  Google Scholar 

  47. Ben-Cohen A, Klang E, Amitai MM, Goldberger J, Greenspan H (2018) Anatomical data augmentation for cnn based pixel-wise classification. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), IEEE, pp 1096–1099

  48. Schmauch B, Herent P, Jehanno P, Dehaene O, Saillard C, Aubé C, Luciani A, Lassau N, Jégou S (2019) Diagnosis of focal liver lesions from ultrasound using deep learning. Diagnostic and Interventional Imaging

  49. Doğantekin A, Özyurt F, Avcı E, Koç M (2019) A novel approach for liver image classification: Ph-c-elm. Measurement 137:332–338

    Article  Google Scholar 

  50. Ben-Cohen A, Diamant I, Klang E, Amitai M, Greenspan H (2016) Fully convolutional network for liver segmentation and lesions detection. In: Deep Learning and Data Labeling for Medical Applications. Springer, pp 77–85

  51. Kline TL, Korfiatis P, Edwards ME, Blais JD, Czerwiec FS, Harris PC, King BF, Torres VE, Erickson BJ (2017) Performance of an artificial multi-observer deep neural network for fully automated segmentation of polycystic kidneys. Journal of digital imaging 30(4):442–448

    Article  Google Scholar 

  52. Yin S, Zhang Z, Li H, Peng Q, You X, Furth SL, Tasian GE, Fan Y (2019) Fully-automatic segmentation of kidneys in clinical ultrasound images using a boundary distance regression network. arXiv preprint arXiv:1901.01982

  53. Kuo C-C, Chang C-M, Liu K-T, Lin W-K, Chiang H-Y, Chung C-W, Ho M-R, Sun P-R, Yang R-L, Chen K-T (2019) Automation of the kidney function prediction and classification through ultrasound-based kidney imaging using deep learning. npj Digital Medicine 2(1):29

    Article  Google Scholar 

  54. AlImran A, Amin MN, Johora FT (2018) Classification of chronic kidney disease using logistic regression, feedforward neural network and wide & deep learning. In: 2018 International Conference on Innovation in Engineering and Technology (ICIET), IEEE, pp 1–6

  55. Salehinejad H, Naqvi S, Colak E, Barfett J, Valaee S (2018) Cylindrical transform: 3d semantic segmentation of kidneys with limited annotated images. In: 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), IEEE, pp 539–543

  56. Marsh JN, Matlock MK, Kudose S, Liu T-C, Stappenbeck TS, Gaut JP, Swamidass SJ (2018) Deep learning global glomerulosclerosis in transplant kidney frozen sections. IEEE transactions on medical imaging 37(12):2718–2728

    Article  Google Scholar 

  57. Pedraza A, Gallego J, Lopez S, Gonzalez L, Laurinavicius A, Bueno G (2017) Glomerulus classification with convolutional neural networks. In: Annual Conference on Medical Image Understanding and Analysis, Springer, pp 839–849

  58. Roth HR, Farag A, Lu L, Turkbey EB, Summers RM (2015) Deep convolutional networks for pancreas segmentation in ct imaging. In: Medical Imaging 2015: Image Processing, vol 9413, International Society for Optics and Photonics, p 94131G

  59. Sekaran K, Chandana P, Krishna NM, Kadry S (2019) Deep learning convolutional neural network (cnn) with gaussian mixture model for predicting pancreatic cancer. Multimed Tools Appl 79:1–15

    Google Scholar 

  60. Oktay O, Schlemper J, Folgoc LL, Lee M, Heinrich M, Misawa K, Mori K, McDonagh S, Hammerla NY, Kainz B, et al. (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999

  61. Shichijo S, Nomura S, Aoyama K, Nishikawa Y, Miura M, Shinagawa T, Takiyama H, Tanimoto T, Ishihara S, Matsuo K, et al. (2017) Application of convolutional neural networks in the diagnosis of helicobacter pylori infection based on endoscopic images. EBioMedicine 25:106–111

    Article  Google Scholar 

  62. Garcia E, Hermoza R, Castanon CB, Cano L, Castillo M, Castanneda C (2017) Automatic lymphocyte detection on gastric cancer ihc images using deep learning. In: 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS), IEEE, pp 200–204

  63. Horie Y, Yoshio T, Aoyama K, Yoshimizu S, Horiuchi Y, Ishiyama A, Hirasawa T, Tsuchida T, Ozawa T, Ishihara S, et al. (2019) Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks. Gastrointestinal endoscopy 89(1):25–32

    Article  Google Scholar 

  64. Itoh T, Kawahira H, Nakashima H, Yata N (2018) Deep learning analyzes helicobacter pylori infection by upper gastrointestinal endoscopy images. Endoscopy international open 6(02):E139–E144

    Article  Google Scholar 

  65. Li Y, Li X, Xie X, Shen L (2018) Deep learning based gastric cancer identification. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), IEEE, pp 182–185

  66. Zhu Y, Wang Q-C, Xu M-D, Zhang Z, Cheng J, Zhong Y-S, Zhang Y-Q, Chen W-F, Yao L-Q, Zhou P-H, et al. (2019) Application of convolutional neural network in the diagnosis of the invasion depth of gastric cancer based on conventional endoscopy. Gastrointestinal endoscopy 89(4):806–815

    Article  Google Scholar 

  67. Rehman A, Naz S, Razzak MI (2019) Writer identification using machine learning approaches: a comprehensive review. Multimedia Tools and Applications 78(8):10889–10931

    Article  Google Scholar 

  68. Bibi K, Naz S, Rehman A (2019) Biometric signature authentication using machine learning techniques: Current trends, challenges and opportunities. Multimed Tools Appl 79:1–52

    Google Scholar 

  69. Yang W, Lu Z, Yu M, Huang M, Feng Q, Chen W (2012) Content-based retrieval of focal liver lesions using bag-of-visual-words representations of single-and multiphase contrast-enhanced ct images. Journal of digital imaging 25(6):708–719

    Article  Google Scholar 

  70. Wang J, Han X-H, Xu Y, Lin L, Hu H, Jin C, Chen Y-W (2017) Sparse codebook model of local structures for retrieval of focal liver lesions using multiphase medical images. International journal of biomedical imaging, 2017. https://doi.org/10.1155/2017/1413297

  71. AlSadeque Z, Khan TI, Hossain QD, Turaba MY (2019) Automated detection and classification of liver cancer from ct images using hog-svm model. In: 2019 5th International Conference on Advances in Electrical Engineering (ICAEE), IEEE, pp 21–26

  72. Pole R, Rajeswari P (2017) Analysis of liver anomalies in ct image using feature extraction method glrlm and phog algorithm. IJERT NLPGPS-17, 5(21)

  73. Pal R, Saraswat M (2019) Histopathological image classification using enhanced bag-of-feature with spiral biogeography-based optimization. Appl Intell 49(9):3406–3424

    Article  Google Scholar 

  74. Bevilacqua V, Pietroleonardo N, Triggiani V, Brunetti A, DiPalma AM, Rossini M, Gesualdo L (2017) An innovative neural network framework to classify blood vessels and tubules based on haralick features evaluated in histological images of kidney biopsy. Neurocomputing 228:143–153

    Article  Google Scholar 

  75. Korkmaz SA, Bínol H, Akçiçek A, Korkmaz MF (2017) A expert system for stomach cancer images with artificial neural network by using hog features and linear discriminant analysis: Hog_lda_ann. In: 2017 IEEE 15th International Symposium on Intelligent Systems and Informatics (SISY), IEEE, pp 000327–000332

  76. Korkmaz SA, Binol H (2018) Classification of molecular structure images by using ann, rf, lbp, hog, and size reduction methods for early stomach cancer detection. J Mol Struct 1156:255–263

    Article  Google Scholar 

  77. Vorontsov E, Cerny M, Régnier P, DiJorio L, Pal CJ, Lapointe R, Vandenbroucke-Menu F, Turcotte S, Kadoury S, Tang A (2019) Deep learning for automated segmentation of liver lesions at ct in patients with colorectal cancer liver metastases. Radiology: Artificial Intelligence 1(2):180014

    Google Scholar 

  78. Christ PF, Elshaer M EA, Ettlinger F, Tatavarty S, Bickel M, Bilic P, Rempfler M, Armbruster M, Hofmann F, DAnastasi M, et al. (2016) Automatic liver and lesion segmentation in ct using cascaded fully convolutional neural networks and 3d conditional random fields. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 415–423

  79. Christ PF, Ettlinger F, Grün F, Elshaera M EA, Lipkova J, Schlecht S, Ahmaddy F, Tatavarty S, Bickel M, Bilic P, et al. (2017) Automatic liver and tumor segmentation of ct and mri volumes using cascaded fully convolutional neural networks. arXiv preprint arXiv:1702.05970

  80. Sun C, Guo S, Zhang H, Li J, Chen M, Ma S, Jin L, Liu X, Li X, Qian X (2017) Automatic segmentation of liver tumors from multiphase contrast-enhanced ct images based on fcns. Artificial intelligence in medicine 83:58–66

    Article  Google Scholar 

  81. Han X (2017) Automatic liver lesion segmentation using a deep convolutional neural network method. arXiv preprint arXiv:1704.07239

  82. Li X, Chen H, Qi X, Dou Q, Fu C-W, Heng P-A (2018) H-denseunet: hybrid densely connected unet for liver and tumor segmentation from ct volumes. IEEE transactions on medical imaging 37(12):2663–2674

    Article  Google Scholar 

  83. Zheng Q, Furth SL, Tasian GE, Fan Y (2019) Computer-aided diagnosis of congenital abnormalities of the kidney and urinary tract in children based on ultrasound imaging data by integrating texture image features and deep transfer learning image features. Journal of pediatric urology 15(1):75–e1

    Article  Google Scholar 

  84. Zheng Q, Tastan G, Fan Y (2018) Transfer learning for diagnosis of congenital abnormalities of the kidney and urinary tract in children based on ultrasound imaging data. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), IEEE, pp 1487–1490

  85. Kannan S, Morgan LA, Liang B, Cheung MG, Lin CQ, Mun D, Nader RG, Belghasem ME, Henderson JM, Francis JM, et al. (2019) Segmentation of glomeruli within trichrome images using deep learning. Kidney International Reports

  86. Bevilacqua V, Brunetti A, Cascarano GD, Palmieri F, Guerriero A, Moschetta M (2018) A deep learning approach for the automatic detection and segmentation in autosomal dominant polycystic kidney disease based on magnetic resonance images. In: International Conference on Intelligent Computing, Springer, pp 643–649

  87. Sharma K, Rupprecht C, Caroli A, Aparicio MC, Remuzzi A, Baust M, Navab N (2017) Automatic segmentation of kidneys using deep learning for total kidney volume quantification in autosomal dominant polycystic kidney disease. Scientific reports 7(1):2049

    Article  Google Scholar 

  88. Li H, Lin K, Reichert M, Xu L, Braren R, Fu D, Schmid R, Li J, Menze B, Shi K (2018) Differential diagnosis for pancreatic cysts in ct scans using densely-connected convolutional networks. arXiv preprint arXiv:1806.01023

  89. Zhu Z, Xia Y, Shen W, Fishman EK, Yuille AL (2017) A 3d coarse-to-fine framework for automatic pancreas segmentation. arXiv preprint arXiv:1712.00201; 02

  90. Zhu Z, Xia Y, Xie L, Fishman EK, Yuille AL (2019) Multi-scale coarse-to-fine segmentation for screening pancreatic ductal adenocarcinoma. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 3–12

  91. Man Y, Huang Y, Li J FX, Wu F (2019) Deep q learning driven ct pancreas segmentation with geometry-aware u-net. IEEE transactions on medical imaging

  92. Zhang X, Hu W, Chen F, Liu J, Yang Y, Wang L, Duan H, Si J (2017) Gastric precancerous diseases classification using cnn with a concise model. PloS one 12(9):e0185508

    Article  Google Scholar 

  93. Lee JH, Kim YJ, Kim YW, Park S, Choi Y-, Kim YJ, Park DK, Kim KG, Chung J-W (2019) Spotting malignancies from gastric endoscopic images using deep learning. Surgical endoscopy, pp 1–8

  94. Takiyama H, Ozawa T, Ishihara S, Fujishiro M, Shichijo S, Nomura S, Miura M, Tada T (2018) Automatic anatomical classification of esophagogastroduodenoscopy images using deep convolutional neural networks. Scientific reports 8(1):7497

    Article  Google Scholar 

  95. Fu K-S, Mui JK (1981) A survey on image segmentation. Pattern recognition 13(1):3–16

    Article  MathSciNet  Google Scholar 

  96. Kumar N (2018) Thresholding in salient object detection: a survey. Multimedia Tools and Applications 77(15):19139–19170

    Article  Google Scholar 

  97. Litjens G, Kooi T, Bejnordi BE, Setio A AA, Ciompi F, Ghafoorian M, Van DerLaak JA, VanGinneken B, Sánchez CI (2017) A survey on deep learning in medical image analysis. Medical image analysis 42:60–88

    Article  Google Scholar 

  98. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

  99. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition

  100. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  101. Targ S, Almeida D, Lyman K (2016) Resnet in resnet: Generalizing residual architectures. arXiv preprint arXiv:1603.08029

  102. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9

  103. Huang G, Liu Z, Van DerMaaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700–4708

  104. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818–2826

  105. Chollet F (2017) Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1251–1258

  106. Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520

  107. Rehman A, Naz S, Razzak MI, Hameed IA (2019) Automatic visual features for writer identification: a deep learning approach. IEEE Access 7:17149–17157

    Article  Google Scholar 

  108. Rehman A, Naz S, Razzak MI, Akram F, Imran M (2019) A deep learning-based framework for automatic brain tumors classification using transfer learning. Circuits, Systems, and Signal Processing, pp 1–19

  109. Naz A RS, Naseem U, Razzak I, Hameed IA Deep autoencoder-decoder framework for semantic segmentation of brain tumor. Australian Journal of Intelligent Information Processing Systems, pp 53

  110. Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431–3440

  111. Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241

  112. Jégou S, Drozdzal M, Vazquez D, Romero A, Bengio Y (2017) The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 11–19

  113. Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2017) Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence 40(4):834–848

    Article  Google Scholar 

  114. Takikawa T, Acuna D, Jampani V, Fidler S (2019) Gated-scnn: Gated shape cnns for semantic segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, pp 5229–5238

  115. Fan D-P, Cheng M-M, Liu J-J, Gao S-H, Hou Q, Borji A (2018) Salient objects in clutter: Bringing salient object detection to the foreground. In: Proceedings of the European conference on computer vision (ECCV), pp 186–202

  116. Achanta R, Hemami S, Estrada F, Susstrunk S (2009) Frequency-tuned salient region detection. In: 2009 IEEE conference on computer vision and pattern recognition, IEEE, pp 1597–1604

  117. Fan D-P, Gong C, Cao Y, Ren B, Cheng M-M, Borji A (2018) Enhanced-alignment measure for binary foreground map evaluation. arXiv preprint arXiv:1805.10421

  118. Hu P, Wu F, Peng J, Bao Y, Chen F, Kong D (2017) Automatic abdominal multi-organ segmentation using deep convolutional neural network and time-implicit level sets. International journal of computer assisted radiology and surgery 12 (3):399–411

    Article  Google Scholar 

  119. Sharma H, Zerbe N, Klempert I, Hellwich O, Hufnagl P (2017) Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology. Comput Med Imaging Graph 61:2–13

    Article  Google Scholar 

  120. Jimenez-del Toro O, Müller H, Krenn M, Gruenberg K, Taha AA, Winterstein M, Eggel I, Foncubierta-Rodríguez A, Goksel O, Jakab A, et al. (2016) Cloud-based evaluation of anatomical structure segmentation and landmark detection algorithms: Visceral anatomy benchmarks. IEEE transactions on medical imaging 35(11):2459–2475

    Article  Google Scholar 

  121. Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, Downey P, Elliott P, Green J, Landray M, et al. (2015) Uk biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS medicine 12(3):e1001779

    Article  Google Scholar 

  122. deBakker BS, deJong KH, Hagoort J, deBree K, Besselink CT, deKanter FroukjeEC, Veldhuis T, Bais B, Schildmeijer R, Ruijter JM, et al. (2016) An interactive three-dimensional digital atlas and quantitative database of human development. Science 354(6315):aag0053

    Article  Google Scholar 

  123. Gholipour A, Rollins CK, Velasco-Annis C, Ouaalam A, Akhondi-Asl A, Afacan O, Ortinau CM, Clancy S, Limperopoulos C, Yang E, et al. (2017) A normative spatiotemporal mri atlas of the fetal brain for automatic segmentation and analysis of early brain growth. Scientific reports 7(1):476

    Article  Google Scholar 

  124. Rehman A, Naz S, Razzak I (2020) Leveraging big data analytics in healthcare enhancement: Trends, challenges and opportunities. arXiv preprint arXiv:2004.09010

  125. Wang L, Lu H, Ruan X, Yang M-H (2015) Deep networks for saliency detection via local estimation and global search. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3183–3192

  126. Zhao R, Ouyang W, Li H, Wang X (2015) Saliency detection by multi-context deep learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1265–1274

  127. Li G, Yu Y (2015) Visual saliency based on multiscale deep features. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5455–5463

  128. Li G, Yu Y (2016) Deep contrast learning for salient object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 478–487

  129. Lee G, Tai Y-W, Kim J (2016) Deep saliency with encoded low level distance map and high level features. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , pp 660–668

  130. Liu N, Han J (2016) Dhsnet: Deep hierarchical saliency network for salient object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 678–686

  131. Wang L, Wang L, Lu H, Zhang P, Ruan X (2016) Saliency detection with recurrent fully convolutional networks. In: European conference on computer vision, Springer, pp 825–841

  132. Chen T, Lin L, Liu L, Luo X, Li X (2016) Disc: Deep image saliency computing via progressive representation learning. IEEE transactions on neural networks and learning systems 27(6):1135–1149

    Article  MathSciNet  Google Scholar 

  133. Zhang J, Dai Y, Porikli F (2017) Deep salient object detection by integrating multi-level cues. In: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), IEEE, pp 1–10

  134. Luo Z, Mishra A, Achkar A, Eichel J, Li S, Jodoin P-M (2017) Non-local deep features for salient object detection. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, pp 6609–6617

  135. Zhang P, Wang D, Lu H, Wang H, Ruan X (2017) Amulet: Aggregating multi-level convolutional features for salient object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp 202–211

  136. Li X, Zhao L, Wei L, Yang M-H, Wu F, Zhuang Y, Ling H, Wang J (2016) Deepsaliency: Multi-task deep neural network model for salient object detection. IEEE transactions on image processing 25(8):3919–3930

    Article  MathSciNet  MATH  Google Scholar 

  137. Wang L, Lu H, Wang Y, Feng M, Wang D, Yin B, Ruan X (2017) Learning to detect salient objects with image-level supervision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 136–145

  138. Li G, Xie Y, Lin L, Yu Y (2017) Instance-level salient object segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2386–2395

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Acknowledgments

The authors are grateful to Dr. Kashif Bilal and Hafiza Zuha Ather for their valuable suggestions, technical language editing, and proofreading. We are also thankful to Dr. Saeeda Naz for her administrative support and writing assistance.

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  1. Department of Computer Science, Comsats Institute of Information Technology, Islamabad, Pakistan

    Arshia Rehman & Fiaz Gul Khan

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  1. Arshia Rehman
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  2. Fiaz Gul Khan
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Rehman, A., Khan, F.G. A deep learning based review on abdominal images. Multimed Tools Appl 80, 30321–30352 (2021). https://doi.org/10.1007/s11042-020-09592-0

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