Use of semantic segmentation for mapping Sargassum on beaches.
Ontology highlight
ABSTRACT:
SUBMITTER: Arellano-Verdejo J
PROVIDER: S-EPMC9188770 | biostudies-literature | 2022
REPOSITORIES: biostudies-literature
Publications
Use of semantic segmentation for mapping <i>Sargassum</i> on beaches.
Arellano-Verdejo Javier J Santos-Romero Martin M Lazcano-Hernandez Hugo E HE
PeerJ 20220609
The unusual arrival of <i>Sargassum</i> on Caribbean beaches is an emerging problem that has generated numerous challenges. The monitoring, visualization, and estimation of <i>Sargassum</i> coverage on the beaches remain a constant complication. This study proposes a new mapping methodology to estimate <i>Sargassum</i> coverage on the beaches. Semantic segmentation of geotagged photographs allows the generation of accurate maps showing the percent coverage of <i>Sargassum</i>. The first dataset ...[more]
Similar Datasets
Project description:The atypical arrival of pelagic Sargassum to the Mexican Caribbean beaches has caused considerable economic and ecological damage. Furthermore, it has raised new challenges for monitoring the coastlines. Historically, satellite remote-sensing has been used for Sargassum monitoring in the ocean; nonetheless, limitations in the temporal and spatial resolution of available satellite platforms do not allow for near real-time monitoring of this macro-algae on beaches. This study proposes an innovative approach for monitoring Sargassum on beaches using Crowdsourcing for imagery collection, deep learning for automatic classification, and geographic information systems for visualizing the results. We have coined this collaborative process "Collective View". It offers a geotagged dataset of images illustrating the presence or absence of Sargassum on beaches located along the northern and eastern regions in the Yucatan Peninsula, in Mexico. This new dataset is the largest of its kind in surrounding areas. As part of the design process for Collective View, three convolutional neural networks (LeNet-5, AlexNet and VGG16) were modified and retrained to classify images, according to the presence or absence of Sargassum. Findings from this study revealed that AlexNet demonstrated the best performance, achieving a maximum recall of 94%. These results are good considering that the training was carried out using a relatively small set of unbalanced images. Finally, this study provides a first approach to mapping the Sargassum distribution along the beaches using the classified geotagged images and offers novel insight into how we can accurately map the arrival of algal blooms along the coastline.
| S-EPMC8157248 | biostudies-literature
Project description:The development of compact and energy-efficient wearable sensors has led to an increase in the availability of biosignals. To effectively and efficiently analyze continuously recorded and multidimensional time series at scale, the ability to perform meaningful unsupervised data segmentation is an auspicious target. A common way to achieve this is to identify change-points within the time series as the segmentation basis. However, traditional change-point detection algorithms often come with drawbacks, limiting their real-world applicability. Notably, they generally rely on the complete time series to be available and thus cannot be used for real-time applications. Another common limitation is that they poorly (or cannot) handle the segmentation of multidimensional time series. Consequently, the main contribution of this work is to propose a novel unsupervised segmentation algorithm for multidimensional time series named Latent Space Unsupervised Semantic Segmentation (LS-USS), which was designed to easily work with both online and batch data. Latent Space Unsupervised Semantic Segmentation addresses the challenge of multivariate change-point detection by utilizing an autoencoder to learn a 1-dimensional latent space on which change-point detection is then performed. To address the challenge of real-time time series segmentation, this work introduces the Local Threshold Extraction Algorithm (LTEA) and a "batch collapse" algorithm. The "batch collapse" algorithm enables Latent Space Unsupervised Semantic Segmentation to process streaming data by dividing it into manageable batches, while Local Threshold Extraction Algorithm is employed to detect change-points in the time series whenever the computed metric by Latent Space Unsupervised Semantic Segmentation exceeds a predefined threshold. By using these algorithms in combination, our approach is able to accurately segment time series data in real-time, making it well-suited for applications where timely detection of changes is critical. When evaluating Latent Space Unsupervised Semantic Segmentation on a variety of real-world datasets the Latent Space Unsupervised Semantic Segmentation systematically achieves equal or better performance than other state-of-the-art change-point detection algorithms it is compared to in both offline and real-time settings.
| S-EPMC10166858 | biostudies-literature
Project description:Having access to accurate and recent digital twins of infrastructure assets benefits the renovation, maintenance, condition monitoring, and construction planning of infrastructural projects. There are many cases where such a digital twin does not yet exist, such as for legacy structures. In order to create such a digital twin, a mobile laser scanner can be used to capture the geometric representation of the structure. With the aid of semantic segmentation, the scene can be decomposed into different object classes. This decomposition can then be used to retrieve cad models from a cad library to create an accurate digital twin. This study explores three deep-learning-based models for semantic segmentation of point clouds in a practical real-world setting: PointNet++, SuperPoint Graph, and Point Transformer. This study focuses on the use case of catenary arches of the Dutch railway system in collaboration with Strukton Rail, a major contractor for rail projects. A challenging, varied, high-resolution, and annotated dataset for evaluating point cloud segmentation models in railway settings is presented. The dataset contains 14 individually labelled classes and is the first of its kind to be made publicly available. A modified PointNet++ model achieved the best mean class Intersection over Union (IoU) of 71% for the semantic segmentation task on this new, diverse, and challenging dataset.
| S-EPMC9824632 | biostudies-literature
Project description:Angiogenesis is the development of new blood vessels from pre-existing ones. It is a complex multifaceted process that is essential for the adequate functioning of human organisms. The investigation of angiogenesis is conducted using various methods. One of the most popular and most serviceable of these methods in vitro is the short-term culture of endothelial cells on Matrigel. However, a significant disadvantage of this method is the manual analysis of a large number of microphotographs. In this regard, it is necessary to develop a technique for automating the annotation of images of capillary-like structures. Despite the increasing use of deep learning in biomedical image analysis, as far as we know, there still has not been a study on the application of this method to angiogenesis images. To the best of our knowledge, this article demonstrates the first tool based on a convolutional Unet++ encoder-decoder architecture for the semantic segmentation of in vitro angiogenesis simulation images followed by the resulting mask postprocessing for data analysis by experts. The first annotated dataset in this field, AngioCells, is also being made publicly available. To create this dataset, participants were recruited into a markup group, an annotation protocol was developed, and an interparticipant agreement study was carried out.
| S-EPMC9866671 | biostudies-literature
Project description:We present an algorithm for the per-voxel semantic segmentation of a three-dimensional volume. At the core of our algorithm is a novel "pyramid context" feature, a descriptive representation designed such that exact per-voxel linear classification can be made extremely efficient. This feature not only allows for efficient semantic segmentation but enables other aspects of our algorithm, such as novel learned features and a stacked architecture that can reason about self-consistency. We demonstrate our technique on 3D fluorescence microscopy data of Drosophila embryos for which we are able to produce extremely accurate semantic segmentations in a matter of minutes, and for which other algorithms fail due to the size and high-dimensionality of the data, or due to the difficulty of the task.
| S-EPMC4445881 | biostudies-literature
Project description:A growing body of evidence suggests that gene flow between closely related species is a widespread phenomenon. Alleles that introgress from one species into a close relative are typically neutral or deleterious, but sometimes confer a significant fitness advantage. Given the potential relevance to speciation and adaptation, numerous methods have therefore been devised to identify regions of the genome that have experienced introgression. Recently, supervised machine learning approaches have been shown to be highly effective for detecting introgression. One especially promising approach is to treat population genetic inference as an image classification problem, and feed an image representation of a population genetic alignment as input to a deep neural network that distinguishes among evolutionary models (i.e. introgression or no introgression). However, if we wish to investigate the full extent and fitness effects of introgression, merely identifying genomic regions in a population genetic alignment that harbor introgressed loci is insufficient-ideally we would be able to infer precisely which individuals have introgressed material and at which positions in the genome. Here we adapt a deep learning algorithm for semantic segmentation, the task of correctly identifying the type of object to which each individual pixel in an image belongs, to the task of identifying introgressed alleles. Our trained neural network is thus able to infer, for each individual in a two-population alignment, which of those individual's alleles were introgressed from the other population. We use simulated data to show that this approach is highly accurate, and that it can be readily extended to identify alleles that are introgressed from an unsampled "ghost" population, performing comparably to a supervised learning method tailored specifically to that task. Finally, we apply this method to data from Drosophila, showing that it is able to accurately recover introgressed haplotypes from real data. This analysis reveals that introgressed alleles are typically confined to lower frequencies within genic regions, suggestive of purifying selection, but are found at much higher frequencies in a region previously shown to be affected by adaptive introgression. Our method's success in recovering introgressed haplotypes in challenging real-world scenarios underscores the utility of deep learning approaches for making richer evolutionary inferences from genomic data.
| S-EPMC10906877 | biostudies-literature
Project description:A growing body of evidence suggests that gene flow between closely related species is a widespread phenomenon. Alleles that introgress from one species into a close relative are typically neutral or deleterious, but sometimes confer a significant fitness advantage. Given the potential relevance to speciation and adaptation, numerous methods have therefore been devised to identify regions of the genome that have experienced introgression. Recently, supervised machine learning approaches have been shown to be highly effective for detecting introgression. One especially promising approach is to treat population genetic inference as an image classification problem, and feed an image representation of a population genetic alignment as input to a deep neural network that distinguishes among evolutionary models (i.e. introgression or no introgression). However, if we wish to investigate the full extent and fitness effects of introgression, merely identifying genomic regions in a population genetic alignment that harbor introgressed loci is insufficient-ideally we would be able to infer precisely which individuals have introgressed material and at which positions in the genome. Here we adapt a deep learning algorithm for semantic segmentation, the task of correctly identifying the type of object to which each individual pixel in an image belongs, to the task of identifying introgressed alleles. Our trained neural network is thus able to infer, for each individual in a two-population alignment, which of those individual's alleles were introgressed from the other population. We use simulated data to show that this approach is highly accurate, and that it can be readily extended to identify alleles that are introgressed from an unsampled "ghost" population, performing comparably to a supervised learning method tailored specifically to that task. Finally, we apply this method to data from Drosophila, showing that it is able to accurately recover introgressed haplotypes from real data. This analysis reveals that introgressed alleles are typically confined to lower frequencies within genic regions, suggestive of purifying selection, but are found at much higher frequencies in a region previously shown to be affected by adaptive introgression. Our method's success in recovering introgressed haplotypes in challenging real-world scenarios underscores the utility of deep learning approaches for making richer evolutionary inferences from genomic data.
| S-EPMC9979274 | biostudies-literature
Project description:Although semantic segmentation is widely employed in autonomous driving, its performance in segmenting road surfaces falls short in complex traffic environments. This study proposes a frequency-based semantic segmentation with a transformer (FSSFormer) based on the sensitivity of semantic segmentation to frequency information. Specifically, we propose a weight-sharing factorized attention to select important frequency features that can improve the segmentation performance of overlapping targets. Moreover, to address boundary information loss, we used a cross-attention method combining spatial and frequency features to obtain further detailed pixel information. To improve the segmentation accuracy in complex road scenarios, we adopted a parallel-gated feedforward network segmentation method to encode the position information. Extensive experiments demonstrate that the mIoU of FSSFormer increased by 2% compared with existing segmentation methods on the Cityscapes dataset.
| S-EPMC11623202 | biostudies-literature
Project description:Segmentation of the prostate into specific anatomical zones is important for radiological assessment of prostate cancer in magnetic resonance imaging (MRI). Of particular interest is segmenting the prostate into two regions of interest: the central gland (CG) and peripheral zone (PZ). In this paper, we propose to integrate an anatomical atlas of prostate zone shape into a deep learning semantic segmentation framework to segment the CG and PZ in T2-weighted MRI. Our approach incorporates anatomical information in the form of a probabilistic prostate zone atlas and utilizes a dynamically controlled hyperparameter to combine the atlas with the semantic segmentation result. In addition to providing significantly improved segmentation performance, this hyperparameter is capable of being dynamically adjusted during the inference stage to provide users with a mechanism to refine the segmentation. We validate our approach using an external test dataset and demonstrate Dice similarity coefficient values (mean±SD) of 0.91±0.05 for the CG and 0.77±0.16 for the PZ that significantly improves upon the baseline segmentation results without the atlas. All code is publicly available on GitHub: https://github.com/OnofreyLab/prostate_atlas_segm_miccai2022.
| S-EPMC10711803 | biostudies-literature
Project description:This paper introduces a video dataset for semantic segmentation of road potholes. This dataset contains 619 high-resolution videos captured in January 2023, covering locations in eight villages within the Hulu Sungai Tengah regency of South Kalimantan, Indonesia. The dataset is divided into three main folders, namely train, val, and test. The train, val, and test folders contain 372 videos for training, 124 videos for validation, and 123 videos for testing, respectively. Each of these main folders has two subfolders, ``RGB'' for the video in the RGB format and ``mask'' for the ground truth segmentation. These videos are precisely two seconds long, containing 48 frames each, and all are in MP4 format. The dataset offers remarkable flexibility, accommodating various research needs, from full-video segmentation to frame extraction. It enables researchers to create ground truth annotations and change the combination of videos in the folders according to their needs. This resource is an asset for researchers, engineers, policymakers, and anyone interested in advancing algorithms for pothole detection and analysis. This dataset allows for benchmarking semantic segmentation algorithms, conducting comparative studies on pothole detection methods, and exploring innovative approaches, offering valuable contributions to the computer vision community.
| S-EPMC10867608 | biostudies-literature