Open tools for dendrochronology. Advances in sample digitization and deep learning methods for image segmentation

Dendrochronological techniques are paramount in forest research. The current climate change scenario and the central role of forests in biogeophysical cycles enforce the importance of novel techniques to get accurate data from trees and their relationship with the environment in faster ways. Recent technological advances and the place of open source software and hardware are making free, user-developed tools for forest research available to the research community. The aim of this Ph.D. thesis is the development of tools for image acquisition and data collection in dendrochronology based on open source software and hardware. Thus, four different tools for dendrochronological research are presented in five different chapters. The first chapter focuses on the development of a do-it-yourself tool based on open source hardware for image acquisition and wood sample digitization at high resolution. We used open hardware equipment from Arduino and Python programming to develop CaptuRING and published the entire free open source tool as: "CaptuRING: A Do-It-Yourself tool for wood sample digitization" in Methods in Ecology and Evolution, 2022; 13:1185-1191. Furthermore, the original software was registered in the Registro General de Propiedad Intelectual (00/2022/737) of Ministerio de Cultura y Deporte (Spain). The second chapter presents "How to build and install your own CaptuRING". This contribution presents a series of videos with a step-by-step guide to promote the use of CaptuRING in the research community. The manuscript and related videos have been submitted for publication. The third chapter describes ρ-MtreeRing. This free and open-source software, which is written in R, analyzes X-ray films from dendrochronological samples to get microdensity values automatically segmented through a graphical user interface. The open source tool and manuscript are published as: "ρ-MtreeRing. A graphical user interface for X-ray microdensity analysis" in Forests. 2021; 12(10):1405. The fourth chapter describes the potential of deep learning methods to automatically segment xylem vessels. We trained three different convolutional neural networks to segment vessels in stained wood microsections using the Keras framework in Python. Our results demonstrate the potential of these techniques to automatically segment xylem vessels and overcome derived problems from image illumination, which hamper segmentation using classical image segmentation methods. The manuscript is published as "Convolutional neural networks for segmenting xylem vessels in stained cross-sectional images" in: Neural Computing & Applications, 2020; 32:17927-17939. The fifth chapter develops an algorithm to delineate annual ring limits in stained wood microsections of a species with diffuse porous wood using convolutional neural networks. We used Python for image processing and the Keras framework for the algorithm training. The results show the ability of this techniques to obtain accurate tree ring segmentation for quantitative wood anatomy, reaching similar or even outperforming conventional manual delimitation in most of the evaluated cases. The results of this chapter will be presented in the manuscript "Deep Learning for ring bordering in stained cross-sectional images". This PhD Thesis presents four open source tools to get accurate information from wood features to unveil how trees respond to the environment. From digitization at macroscopic perspective, automatic data collection and the development of feature segmentation on microscopic samples. The presented four novel dendrochronological tools based on open source software facilitates forest research in the current climate change scenario.Las técnicas dendrocronológicas son fundamentales en la investigación forestal. El escenario actual de cambio climático y el papel central de los bosques en los ciclos biogeofísicos subrayan la necesidad de nuevas técnicas para obtener de un modo ágil datos precisos de los árboles y de su relación con el medio ambiente. Los recientes avances tecnológicos, además de la disponibilidad actual del software y el hardware de código abierto están poniendo a disposición de la comunidad investigadora herramientas gratuitas desarrolladas por los usuarios para la investigación forestal. El objetivo de esta tesis doctoral es el desarrollo de herramientas para la adquisición de imágenes y la recogida de datos basadas en software y hardware de código abierto para el estudio dendrocronológico. Esta tesis presenta cuatro herramientas diferentes para esta rama científica en cinco capítulos diferentes. El primer capítulo se centra en el desarrollo de una herramienta "hágalo usted mismo" basada en hardware de código abierto para la adquisición de imágenes y la digitalización de muestras de madera a alta resolución. Usamos equipos de hardware abierto de Arduino y programación de Python para desarrollar CaptuRING y publicamos la herramienta completa de código abierto como: "CaptuRING: A Do-It-Yourself tool for wood sample digitization" en Methods in Ecology and Evolution, 2022; 13:1185-1191. Además, el software original fue registrado en el Registro General de Propiedad Intelectual (00/2022/737) del Ministerio de Cultura y Deporte (España). El segundo capítulo presenta "Cómo construir e instalar su propio CaptuRING" ("How to build and install your own CaptuRING"). Esta contribución presenta una serie de vídeos con una guía paso a paso para promover el uso de CaptuRING en la comunidad investigadora. El manuscrito y los vídeos relacionados se han enviado para su publicación. El tercer capítulo describe ρ-MtreeRing. Este software gratuito y de código abierto, que está escrito en R, analiza imágenes de rayos X de muestras dendrocronológicas para obtener valores de microdensidad automáticamente segmentados a través de una sencilla interfaz gráfica de usuario. La herramienta de código abierto y el manuscrito se publicaron como: "ρ-MtreeRing. A graphical user interface for X-ray microdensity analysis" en Forests. 2021; 12(10):1405. El cuarto capítulo describe el potencial de los métodos de aprendizaje profundo para segmentar automáticamente los vasos del xilema. Entrenamos tres redes neuronales convolucionales diferentes para segmentar vasos en cortes histológicos de madera utilizando el marco Keras en Python. Nuestros resultados demuestran el potencial de estas técnicas para segmentar automáticamente los vasos del xilema y superar los problemas derivados de la iluminación de la imagen, que dificultan la labor de métodos clásicos de segmentación de imágenes. El manuscrito se publicó como "Convolutional neural networks for segmenting xylem vessels in stained cross-sectional images" en: Neural Computing & Applications. 2020; 32:17927-17939. El quinto capítulo desarrolla un algoritmo para delinear los límites anuales de los anillos en cortes histológicos de una especie con madera difuso-porosa utilizando redes neuronales convolucionales. Se utilizó Python para el procesamiento de imágenes y el marco Keras para el entrenamiento del algoritmo. Los resultados muestran la capacidad de estas técnicas para obtener una segmentación precisa de los anillos de los árboles para la anatomía cuantitativa de la madera alcanzando, en la mayoría de los casos evaluados, un rendimiento similar o incluso superior a la delimitación manual convencional. Los resultados de este capítulo se presentarán en el manuscrito "Deep Learning for ring bordering in stained cross-sectional images". Esta Tesis Doctoral presenta cuatro herramientas de código abierto para obtener información precisa de las características de la madera investigar cómo los árboles responden al entorno facilitando la investigación en el actual escenario de cambio climático.Escuela de DoctoradoDoctorado en Conservación y Uso Sostenible de Sistemas Forestale

Towards low-complexity wireless technology classification across multiple environments

To cope with the increasing number of co-existing wireless standards, complex machine learning techniques have been proposed for wireless technology classification. However, machine learning techniques in the scientific literature suffer from some shortcomings, namely: (i) they are often trained using data from only a single measurement location, and as such the results do not necessarily generalise and (ii) they typically do not evaluate complexity/accuracy trade-offs of the proposed solutions. To remedy these shortcomings, this paper investigates which resource-friendly approaches are suitable across multiple heterogeneous environments. To this end, the paper designs and evaluates classifiers for LTE, Wi-Fi and DVB-T technologies using multiple datasets to investigate the complexity/accuracy trade-offs between manual feature extraction and automatic feature learning techniques. Our wireless technology classification reaches an accuracy up to 99%. Moreover, we propose the use of data augmentation techniques to extend these results to unseen environments at the cost of only 2% reduction in accuracy. When concerning generalisation capabilities, complex automatic learning techniques surpass simple manual feature extraction approaches. Finally, the complexity of these automatic learning techniques can be significantly reduced by using computationally less intensive received signal strength indicator data while reaching acceptable accuracies in unseen environments (92% vs 97%). (C) 2019 Elsevier B.V. All rights reserved

Enabling Intelligent IoTs for Histopathology Image Analysis Using Convolutional Neural Networks

Medical imaging is an essential data source that has been leveraged worldwide in healthcare systems. In pathology, histopathology images are used for cancer diagnosis, whereas these images are very complex and their analyses by pathologists require large amounts of time and effort. On the other hand, although convolutional neural networks (CNNs) have produced near-human results in image processing tasks, their processing time is becoming longer and they need higher computational power. In this paper, we implement a quantized ResNet model on two histopathology image datasets to optimize the inference power consumption. We analyze classification accuracy, energy estimation, and hardware utilization metrics to evaluate our method. First, the original RGBcolored images are utilized for the training phase, and then compression methods such as channel reduction and sparsity are applied. Our results show an accuracy increase of 6% from RGB on 32-bit (baseline) to the optimized representation of sparsity on RGB with a lower bit-width, i.e., \u3c8:8\u3e. For energy estimation on the used CNN model, we found that the energy used in RGB color mode with 32-bit is considerably higher than the other lower bit-width and compressed color modes. Moreover, we show that lower bit-width implementations yield higher resource utilization and a lower memory bottleneck ratio. This work is suitable for inference on energy-limited devices, which are increasingly being used in the Internet of Things (IoT) systems that facilitate healthcare systems

A convolutional neural network for segmentation of yeast cells without manual training annotations

MOTIVATION: Single-cell time-lapse microscopy is a ubiquitous tool for studying the dynamics of complex cellular processes. While imaging can be automated to generate very large volumes of data, the processing of the resulting movies to extract high-quality single-cell information remains a challenging task. The development of software tools that automatically identify and track cells is essential for realizing the full potential of time-lapse microscopy data. Convolutional neural networks (CNNs) are ideally suited for such applications, but require great amounts of manually annotated data for training, a time-consuming and tedious process. RESULTS: We developed a new approach to CNN training for yeast cell segmentation based on synthetic data and present (i) a software tool for the generation of synthetic images mimicking brightfield images of budding yeast cells and (ii) a convolutional neural network (Mask R-CNN) for yeast segmentation that was trained on a fully synthetic dataset. The Mask R-CNN performed excellently on segmenting actual microscopy images of budding yeast cells, and a density-based spatial clustering algorithm (DBSCAN) was able to track the detected cells across the frames of microscopy movies. Our synthetic data creation tool completely bypassed the laborious generation of manually annotated training datasets, and can be easily adjusted to produce images with many different features. The incorporation of synthetic data creation into the development pipeline of CNN-based tools for budding yeast microscopy is a critical step toward the generation of more powerful, widely applicable and user-friendly image processing tools for this microorganism. AVAILABILITY AND IMPLEMENTATION: The synthetic data generation code can be found at https://github.com/prhbrt/synthetic-yeast-cells. The Mask R-CNN as well as the tuning and benchmarking scripts can be found at https://github.com/ymzayek/yeastcells-detection-maskrcnn. We also provide Google Colab scripts that reproduce all the results of this work. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

Probabilistic spatial analysis in quantitative microscopy with uncertainty-aware cell detection using deep Bayesian regression

The investigation of biological systems with three-dimensional microscopy demands automatic cell identification methods that not only are accurate but also can imply the uncertainty in their predictions. The use of deep learning to regress density maps is a popular successful approach for extracting cell coordinates from local peaks in a postprocessing step, which then, however, hinders any meaningful probabilistic output. We propose a framework that can operate on large microscopy images and output probabilistic predictions (i) by integrating deep Bayesian learning for the regression of uncertainty-aware density maps, where peak detection algorithms generate cell proposals, and (ii) by learning a mapping from prediction proposals to a probabilistic space that accurately represents the chances of a successful prediction. Using these calibrated predictions, we propose a probabilistic spatial analysis with Monte Carlo sampling. We demonstrate this in a bone marrow dataset, where our proposed methods reveal spatial patterns that are otherwise undetectable

Visualizing and Interpreting Feature Reuse of Pretrained CNNs for Histopathology

Reusing the parameters of networks pretrained on large scale datasets of natural images, such as ImageNet, is a common technique in the medical imaging domain. The large variability of objects and classes is, however, drastically reduced in most medical applications where images are dominated by repetitive patterns with, at times, subtle differences between the classes. This paper takes the example of finetuning a pretrained convolutional network on a histopathology task. Because of the reduced visual variability in this application domain, the network mostly learns to detect textures and simple patterns. As a result, the complex structures that maximize the channel activations of deep layers in the pretrained network are not present after finetuning. The learned features seem to be used by the network to spot atypical nuclei in the images, as shown by class activation maps. Finally, texture measures appear discriminative after finetuning, as shown by accurate Regression Concept Vectors