Convolutional neural networks for segmenting xylem vessels in stained cross-sectional images
Tóm tắt
Xylem is a vascular tissue that conducts sap (water and dissolved minerals) from the roots to the rest of the plant while providing physical support and resources. Sap is conducted within dead hollow cells (called vessels in flowering plants) arranged to form long pipes. Once formed, vessels do not change their structure and last from years to millennia. Vessels’ configuration (size, abundance, and spatial pattern) constitutes a record of the plant–environment relationship, and therefore, a tool for monitoring responses at the plant and ecosystem level. This information can be extracted through quantitative anatomy; however, the effort to identify and measure hundreds of thousands of conductive cells is an inconvenience to the progress needed to have solid assessments of the anatomical–environment relationship. In this paper, we propose an automatic methodology based on convolutional neural networks to segment xylem vessels. It includes a post-processing stage based on the use of redundant information to improve the performance of the outcome and make it useful in different sample configurations. Three different neural networks were tested obtaining similar results (pixel accuracy about 90%), which indicates that the methodology can be effectively used for segmentation of xylem vessels into images with non-homogeneous variations of illumination. The development of accurate automatic tools using CNNs would reduce the entry barriers associated with quantitative xylem anatomy expanding the use of this technique by the scientific community.
Tài liệu tham khảo
Malhi Y, Meir P, Brown S (2002) Forests, carbon and global climate. Philos Trans R Soc Lond Ser A Math Phys Eng Sci 360(1797):1567–1591
Cleland EE, Chuine I, Menzel A, Mooney HA, Schwartz MD (2007) Shifting plant phenology in response to global change. Trends Ecol Evol 22(7):357–365
Choat B, Brodribb TJ, Brodersen CR, Duursma RA, López R, Medlyn BE (2018) Triggers of tree mortality under drought. Nature 558(7711):531
Fonti P, von Arx G, García-González I, Eilmann B, Sass-Klaassen U, Gärtner H, Eckstein D (2010) Studying global change through investigation of the plastic responses of xylem anatomy in tree rings. New Phytol 185(1):42–53
Selig B, Luengo H, Bardage S, Borgefors G (2009) Segmentation of highly lignified zones in wood fiber cross-sections. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), vol 5575. LNCS, pp 369–378
Hacke UG, Spicer R, Schreiber SG, Plavcová L (2017) An ecophysiological and developmental perspective on variation in vessel diameter. Plant Cell Environ 40(6):831–845
García-Cervigón AI, Fajardo A, Caetano-Sánchez C, Camarero JJ, Olano JM. Xylem anatomy needs to change, so that conductivity can stay the same: xylem adjustments across elevation and latitude in Nothofagus pumilio
von Arx G, Carrer M (2014) Roxas—a new tool to build centuries-long tracheid-lumen chronologies in conifers. Dendrochronologia 32(3):290–293. https://doi.org/10.1016/j.dendro.2013.12.001
Olano JM, Eugenio M, García-Cervigón AI, Folch M, Rozas V (2012) Quantitative tracheid anatomy reveals a complex environmental control of wood structure in continental mediterranean climate. Int J Plant Sci 173(2):137–149
Speer JH (2010) Fundamentals of tree-ring research. University of Arizona Press, Tucson
Gärtner H, Cherubini P, Fonti P, von Arx G, Schneider L, Nievergelt D, Verstege A, Bast A, Schweingruber FH, Büntgen U (2015) A technical perspective in modern tree-ring research-how to overcome dendroecological and wood anatomical challenges. J Vis Exp JoVE 5(97):e52337
Arx GV, Dietz H (2005) Automated image analysis of annual rings in the roots of perennial forbs. Int J Plant Sci 166(5):723–732
Meijering E (2012) Cell segmentation: 50 years down the road [life sciences]. IEEE Signal Process Mag 29(5):140–145. https://doi.org/10.1109/MSP.2012.2204190
Moëll M, Donaldson L (2001) Comparison of segmentation methods for digital image analysis of confocal microscope images to measure tracheid cell dimensions. IAWA J 22(3):267–288. https://doi.org/10.1163/22941932-90000284
Land A, Wehr M, Roelfs KU, Epkes S, Reichle D, Kauer G (2017) A novel computer-aided tree-ring analysis software (CATS): oak earlywood vessel size reveals a clear spring heat sum response. Trees 31(5):1683–1695. https://doi.org/10.1007/s00468-017-1578-7
Nedzved A, Mitrović AL, Savić A, Mutavdžić D, Radosavljević JS, Pristov JB, Steinbach G, Garab G, Starovoytov V, Radotić K (2018) Automatic image processing morphometric method for the analysis of tracheid double wall thickness tested on juvenile picea omorika trees exposed to static bending. Trees 32(5):1347–1356
Chopin J, Laga H, Huang CY, Heuer S, Miklavcic SJ (2015) RootAnalyzer: a cross-section image analysis tool for automated characterization of root cells and tissues. PLoS ONE 10(9):e0137655. https://doi.org/10.1371/journal.pone.0137655
Wang H, Qi H, Li W, Zhang G, Wang P (2009) A GA-based automatic pore segmentation algorithm. Assoc Computing Machinery, New York
Wang H, Zhang G, Qi H, Ma L (2009) Multi-objective optimization on pore segmentation. In: 2009 fifth international conference on natural computation, vol 4, pp 613–617. https://doi.org/10.1109/ICNC.2009.572
Zhang S, Xu W, Meng Z (2010) Study on method to dissected data of wood cell image. Adv Mater Res 139–141:303–307. https://doi.org/10.4028/www.scientific.net/AMR.139-141.303
Mallik A, Tarrio-Saavedra J, Francisco-Fernandez M, Naya S (2011) Classification of wood micrographs by image segmentation. Chemom Intell Lab Syst 107(2):351–362. https://doi.org/10.1016/j.chemolab.2011.05.005
Guang-Sheng C, Peng Z (2013) Wood cell recognition using geodesic active contour and principal component analysis. Opt Int J Light Electron Opt 124(10):949–952. https://doi.org/10.1016/j.ijleo.2012.02.032
Qi HN, Chen FN, Ma LF (2007) Pore feature segmentation based on mathematical morphology. In: IECON 2007—33rd annual conference of the IEEE industrial electronics society, pp 2474–2477. https://doi.org/10.1109/IECON.2007.4460248
Pan S, Kudo M (2011) Segmentation of pores in wood microscopic images based on mathematical morphology with a variable structuring element. Comput Electron Agric 75(2):250–260. https://doi.org/10.1016/j.compag.2010.11.010
Wunderling A, Ben Targem M, Barbier de Reuille P, Ragni L (2016) Novel tools for quantifying secondary growth. J Exp Bot 68(1):89–95
Hall HC, Fakhrzadeh A, Luengo Hendriks CL, Fischer U (2016) Precision automation of cell type classification and sub-cellular fluorescence quantification from laser scanning confocal images. Front Plant Sci 7:119
Travis A, Hirst D, Chesson A (1996) Automatic classification of plant cells according to tissue type using anatomical features obtained by the distance transform. Ann Bot 78(3):325–331
Brunel G, Borianne P, Subsol G, Jaeger M, Caraglio Y (2012) Automatic characterization of the cell organization in light microscopic images of wood: application to the identification of the cell file. In: 2012 IEEE 4th international symposium on plant growth modeling, simulation, visualization and applications, pp 58–65. https://doi.org/10.1109/PMA.2012.6524813
Brunel G, Borianne P, Subsol G, Jaeger M, Caraglio Y (2014) Automatic identification and characterization of radial files in light microscopy images of wood. Ann Bot 114(4):829–840. https://doi.org/10.1093/aob/mcu119
Kennel P, Subsol G, Gueroult M, Guéroult M, Borianne P (2010) Automatic identification of cell files in light microscopic images of conifer wood. In: 2010 2nd international conference on image processing theory, tools and applications, pp 98–103. https://doi.org/10.1109/IPTA.2010.5586800
Guan X, Sun L, Cao J (2006) Level set method based on improved Mumford–Shah model applied in wood cell image segmentation. In: The 2006 IEEE international joint conference on neural network proceedings, pp 2315–2318. https://doi.org/10.1109/IJCNN.2006.247031
Zhao L, Ma Y (2010) Wood adhesion cell segmentation scheme based on GVF-Snake model. In: International conference on image processing and pattern recognition in industrial engineering, vol 7820, p 78200N. International society for optics and photonics. https://doi.org/10.1117/12.866337
Fernando Espinosa L, Javier Herrera R, Polanco-Tapia C (2015) Segmentation of anatomical elements in wood microscopic images using artificial vision techniques. Maderas-Ciencia Y Tecnol 17(4):735–748. https://doi.org/10.4067/S0718-221X2015005000064
Roncancio HA, Velasco HF, Herrera RJ (2003) Segmentation of wood microanatomy images using multiscale classification. In: Proceedings of the 3rd IEEE international symposium on signal processing and information technology (IEEE Cat. No.03EX795), pp 692–695. https://doi.org/10.1109/ISSPIT.2003.1341215
García-Pedrero A, Gonzalo-Martín C, Lillo-Saavedra M (2017) A machine learning approach for agricultural parcel delineation through agglomerative segmentation. Int J Remote Sens 38(7):1809–1819. https://doi.org/10.1080/01431161.2016.1278312
Wu W, Chen AY, Zhao L, Corso JJ (2014) Brain tumor detection and segmentation in a crf (conditional random fields) framework with pixel-pairwise affinity and superpixel-level features. Int J Comput Assist Radiol Surg 9(2):241–253
Ma B, Ban X, Huang H, Chen Y, Liu W, Zhi Y (2018) Deep learning-based image segmentation for Al-La Alloy microscopic images. Symmetry 10(4):107
Fu H, Xu Y, Lin S, Wong DWK, Liu J (2016) Deepvessel: retinal vessel segmentation via deep learning and conditional random field. In: International conference on medical image computing and computer-assisted intervention, pp 132–139. Springer
Van Valen DA, Kudo T, Lane KM, Macklin DN, Quach NT, DeFelice MM, Maayan I, Tanouchi Y, Ashley EA, Covert MW (2016) Deep learning automates the quantitative analysis of individual cells in live-cell imaging experiments. PLoS Comput Biol 12(11):e1005177
Yao Z, Zhang Z, Xu LQ (2016) Convolutional neural network for retinal blood vessel segmentation. In: 2016 9th international symposium on Computational intelligence and design (ISCID), vol 1. IEEE, pp 406–409
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
Biem A (2014) Neural networks: a review. In: Aggarwal CC (ed) Data classification: algorithms and applications. CRC press, pp 205–244
Garcia-Pedrero A, García-Cervigón A, Caetano C, Calderón-Ramírez S, Olano JM, Gonzalo-Martín C, Lillo-Saavedra M, García-Hidalgo M (2018) Xylem vessels segmentation through a deep learning approach: a first look. In: 2018 IEEE international work conference on bioinspired intelligence (IWOBI), pp 1–9. https://doi.org/10.1109/IWOBI.2018.8464184
Den Bakker I (2017) Python deep learning cookbook: over 75 practical recipes on neural network modeling, reinforcement learning, and transfer learning using Python. Packt Publishing Ltd, Birmingham
Dettmann S, Pérez CA, Thomas FM (2013) Xylem anatomy and calculated hydraulic conductance of four Nothofagus species with contrasting distribution in South-Central Chile. Trees 27(3):685–696
Körner C (2003) Alpine plant life: functional plant ecology of high mountain ecosystems; with 47 tables. Springer, Berlin
Gärtner H, Lucchinetti S, Schweingruber F (2015) A new sledge microtome to combine wood anatomy and tree-ring ecology. IAWA J 36(4):452–459
Iglovikov V, Mushinskiy S, Osin V (2017) Satellite imagery feature detection using deep convolutional neural network: a kaggle competition. arXiv preprint arXiv:1706.06169
Chaurasia A, Culurciello E (2017) Linknet: exploiting encoder representations for efficient semantic segmentation. In: 2017 IEEE visual communications and image processing (VCIP). IEEE, pp 1–4
Lin TY, Dollár P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2117–2125
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
Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press. http://www.deeplearningbook.org
Iglovikov V, Shvets A (2018) Ternausnet: U-net with VGG11 encoder pre-trained on imagenet for image segmentation. arXiv preprint arXiv:1801.05746
He K, Girshick R, Dollár P (2018) Rethinking imagenet pre-training. arXiv preprint arXiv:1811.08883
Chollet F et al (2015) Keras. https://keras.io
Moolayil J (2018) Learn keras for deep neural networks: a fast-track approach to modern deep learning with Python, 1st edn. Apress, Berkely
Rahman MA, Wang Y (2016) Optimizing intersection-over-union in deep neural networks for image segmentation. In: International symposium on visual computing. Springer, pp 234–244
Garcia-Garcia A, Orts-Escolano S, Oprea S, Villena-Martinez V, Garcia-Rodriguez J (2017) A review on deep learning techniques applied to semantic segmentation. arXiv preprint arXiv:1704.06857