Xylem Vessels Segmentation Through a Deep Learning Approach: a First Look

García‐Pedrero, Ángel; García‐Cervigón, Ana I.; Caetano, Cristina; Calderón-Ramírez, Saúl; Olano, José Miguel; Gonzalo-Martín, Consuelo; Lillo‐Saavedra, Mario; García‐Hidalgo, Miguel

doi:10.1109/iwobi.2018.8464184

Cited by 8 publications

(7 citation statements)

References 32 publications

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“…With recent advances in CV and DL technologies, CNNs have shown remarkable achievements in segmenting cells from biomedical microscopic images [155,182,183]. The state-of-the-art techniques are also been applied to the segmentation of plant cells, including wood [184,185]. Garcia-Pedrero et al [185] segmented xylem vessels from cross-sectional micrographs using Unet [155], a multi-scale encoder-decoder model based on CNN.…”

Section: Cell Segmentation Using Deep Learningmentioning

confidence: 99%

“…The state-of-the-art techniques are also been applied to the segmentation of plant cells, including wood [184,185]. Garcia-Pedrero et al [185] segmented xylem vessels from cross-sectional micrographs using Unet [155], a multi-scale encoder-decoder model based on CNN. They reported that the vessel segmentation by Unet was closer to the results of the expert's work using the image analysis tool ROXAS [186] than the classical techniques Otsu's thresholding methods [187] and morphological active contour method [188].…”

Section: Cell Segmentation Using Deep Learningmentioning

confidence: 99%

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Computer vision-based wood identification and its expansion and contribution potentials in wood science: A review

Hwang

Sugiyama

2021

Plant Methods

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The remarkable developments in computer vision and machine learning have changed the methodologies of many scientific disciplines. They have also created a new research field in wood science called computer vision-based wood identification, which is making steady progress towards the goal of building automated wood identification systems to meet the needs of the wood industry and market. Nevertheless, computer vision-based wood identification is still only a small area in wood science and is still unfamiliar to many wood anatomists. To familiarize wood scientists with the artificial intelligence-assisted wood anatomy and engineering methods, we have reviewed the published mainstream studies that used or developed machine learning procedures. This review could help researchers understand computer vision and machine learning techniques for wood identification and choose appropriate techniques or strategies for their study objectives in wood science.

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Section: Cell Segmentation Using Deep Learningmentioning

confidence: 99%

Section: Cell Segmentation Using Deep Learningmentioning

confidence: 99%

Computer vision-based wood identification and its expansion and contribution potentials in wood science: A review

Hwang

Sugiyama

2021

Plant Methods

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“…Initial algorithms correctly identified almost 100% of rings in four conifer species, roughly 85% in six diffuse-porous species, but only 40-50% in two ring-porous species (Fabijańska, Danek, Barniak, & Piórkowski, 2017;Subah, Derminder, & Sanjeev, 2017), despite being based on comparatively small and homogenous training data. Recently, convolutional neural networks have also succeeded at segmenting anatomical sections of wood (Garcia-Pedrero et al, 2018). The integration of visual computing algorithms into WIAD to automate the analysis and reduce labour time, by suggesting ring boundaries to the user after image upload, is under development.…”

Section: Streamline Data Processing Through Automation and Enable Citmentioning

confidence: 99%

The Wood Image Analysis and Dataset (WIAD): open-access visual analysis tools to advance the ecological data revolution

Rademacher

Seyednasrollah

Basler

et al. 2020

Preprint

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Ecological data is collected and shared at an increasingly rapid pace, but it is often shared in inconsistent and untraceable processed forms. Images of wood contain a wealth of information such as colours and textures but are most commonly reduced to ring-width measurements before they can be shared in various common file formats. In fact, no digital libraries of wood samples that have been developed for ecological analysis currently exist.We developed the Wood Image Analysis and Dataset (WIAD) an open-source application including a web-interface to integrate visual analysis of wood samples, such as increment cores, thin sections or x-ray films, basic data processing, and arching of the images and derived data to facilitate transparency and reproducibility in studies using visual characteristics of wood.WIAD provides user-friendly tools to manipulate images of wood samples, mark and measure wood characteristics such as growth increments, density fluctuations, early- and latewood widths, and fire scars, and to visualise, process, and archive images, metadata, and the derived data.WIAD constitutes a step towards the reproducible automation of tree-ring analysis, while establishing a foundation to create improved community developed repositories and enable novel ecological studies harnessing the wealth of existing visual data.

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“…Despite their benefits in image segmentation, to the best of our knowledge, CNNs are just beginning their journey into dendrochronological applications. In [43], the authors present a CNN approach to segment xylem vessels from microscopic images. The approach is based on a fully convolutional network, called Unet [41], trained from 23 images.…”

Section: Introductionmentioning

confidence: 99%

“…This paper is an extended version of work published in [43]. We extended our previous work by considerably increasing the number of images to be studied, and introducing during the prediction phase an improved version of the tile strategy includes test time augmentation (TTA) [44] on three convolutional neural networks.…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural networks for segmenting xylem vessels in stained cross-sectional images

García‐Pedrero

García‐Cervigón

Olano

et al. 2019

Neural Comput & Applic

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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.

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Xylem Vessels Segmentation Through a Deep Learning Approach: a First Look

Cited by 8 publications

References 32 publications

Computer vision-based wood identification and its expansion and contribution potentials in wood science: A review

Computer vision-based wood identification and its expansion and contribution potentials in wood science: A review

The Wood Image Analysis and Dataset (WIAD): open-access visual analysis tools to advance the ecological data revolution

Convolutional neural networks for segmenting xylem vessels in stained cross-sectional images

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