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Advancing underwater binary image segmentation with PDBU-Net: A progressive approach to feature extraction and accuracy improvement

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REGIONAL STUDIES IN MARINE SCIENCE 2025年 83卷

作者： George, Geomol Anusuya, S. SIMATS Engn Chennai India SIMATS Deemed Univ Chennai Tamil Nadu India

Precise binary image segmentation in underwater environments is crucial for marine biology and underwater robotics applications, as fluctuations in lighting, water conditions, and object appearance provide substantial difficulties. To tackle these problems, this research presents PDBU-Net, an innovative deep-learning framework specifically created to improve the extraction of features and the accuracy of segmentation for binary images captured underwater. PDBU-Net incorporates a progressive feature extraction methodology into a strong deep neural network structure, resulting insignificant enhancements in segmentation performance compared to current methods. PDBU-Net was tested extensively and achieved an average Intersection over Union (IoU) of 90.39% and an F-score of 94.94%. The model achieved precision and recall rates of 94.55% and 95.39% respectively, along with an overall accuracy of 95.66%. The results validate the efficacy of PDBU-Net in precisely detecting objects in a wide range of underwater photos, showcasing its successful implementation in real underwater analysis and robotics.

关键词： Underwater images binary segmentation Progressive DenseNet Deep learning Encoder Decoder

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Influence of decoder size for binary segmentation tasks in medical imaging

Influence of decoder size for binary segmentation tasks in m...

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Medical Imaging Conference - Image Processing

作者： van der Putten, Joost van der Sommen, Fons de With, Peter H. N. Eindhoven Univ Technol Eindhoven Netherlands

ISBN: (纸本)9781510633940

Symmetric design of encoder-decoder networks is common in deep learning. For almost all segmentation problems, the output segmentation is vastly less complex compared to the input image. However, the effect of the size of the decoder on segmentation performance has not been investigated in literature. This work investigates the effect of reducing decoder size on binary segmentation performance in a medical imaging application. To this end, we propose a methodology to reduce the size of the decoder in encoder-decoder networks, where residual skip connections are employed in combination with a 1 x 1 convolution instead of concatenations (as employed by U-Net) to achieve models with asymmetric design. The results on the ISIC2017 data set show that the amount of trainable parameters in the decoder can be reduced by up to a factor 100 compared to standard U-Net, while retaining segmentation performance. Additionally, the reduced amount of trainable decoder parameters in the proposed models leads to inference times up to 3 times faster compared to standard U-Net.

关键词： skin lesions binary segmentation encoder-decoder networks model optimization

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Agrast-6: Abridged VGG-Based Reflected Lightweight Architecture for binary segmentation of Depth Images Captured by Kinect

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SENSORS 2022年第17期22卷 6354页

作者： Ryselis, Karolis Blazauskas, Tomas Damasevicius, Robertas Maskeliunas, Rytis Kaunas Univ Technol Fac Informat LT-44249 Kaunas Lithuania

binary object segmentation is a sub-area of semantic segmentation that could be used for a variety of applications. Semantic segmentation models could be applied to solve binary segmentation problems by introducing only two classes, but the models to solve this problem are more complex than actually required. This leads to very long training times, since there are usually tens of millions of parameters to learn in this category of convolutional neural networks (CNNs). This article introduces a novel abridged VGG-16 and SegNet-inspired reflected architecture adapted for binary segmentation tasks. The architecture has 27 times fewer parameters than SegNet but yields 86% segmentation cross-intersection accuracy and 93% binary accuracy. The proposed architecture is evaluated on a large dataset of depth images collected using the Kinect device, achieving an accuracy of 99.25% in human body shape segmentation and 87% in gender recognition tasks.

关键词： depth images convolutional neural network binary segmentation

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Short communication: Detecting possibly frequent change-points: wild binary segmentation 2 and steepest-drop model selection

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JOURNAL OF THE KOREAN STATISTICAL SOCIETY 2020年第4期49卷 1090-1095页

作者： Lund, Robert Shi, Xueheng Univ Calif Santa Cruz Dept Stat Santa Cruz CA 95064 USA Clemson Univ Sch Math & Stat Clemson SC 29634 USA

This article comments on the new version of wild binary segmentation in Fryzlewicz (Ann Stat 42(6):2243-2281, 2014).

关键词： binary segmentation mBIC Steepest-drop to low levels Wild binary segmentation

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binary segmentation for Structural Condition Classification Using Structural Health Monitoring Data

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JOURNAL OF AEROSPACE ENGINEERING 2019年第1期32卷

作者： Wan, Hua-Ping Ni, Yi-Qing Hong Kong Polytech Univ Dept Civil & Environm Engn Kowloon Hong Kong Peoples R China

Structural health monitoring (SHM) is the process of conducting structural condition diagnosis and prognosis based on appropriate analyses of in situ measurement data. Direct assessment of structural condition using time series response measurements can be classified as a type of statistical pattern recognition, in which structural condition is evaluated by comparing the statistical features of current data with those of baseline data. The philosophy behind this approach is that the time series response acquired under different structural conditions presents different statistical characteristics. As a consequence, the key step in structural condition classification is to detect the points at which the statistical properties of a time series response change;this is referred to as change-point analysis. The present study proposes the use of a computationally efficient binary segmentation (BS) approach for change-point detection in order to classify and assess structural health condition. The proposed approach, which falls into the category of data-driven diagnosis, does not require knowledge about the structure and is appealing for attaining an automated SHM system. The practicality and effectiveness are illustrated through real-world monitoring data acquired from a cable-stayed bridge and a high-speed train, both of which experienced structural damage/degradation over their service lives. (C) 2018 American Society of Civil Engineers.

关键词： Structural health monitoring Condition classification Change-point analysis binary segmentation Bridge High-speed rail

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Novel electromyography signal envelopes based on binary segmentation

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2018年第0期45卷 225-236页

作者： Guerrero, J. A. Castillo-Galvan, M. A. Macias-Diaz, J. E. Univ Autonoma Aguascalientes Dept Estadist Ave Univ 940Ciudad Univ Aguascalientes 20131 Ags Mexico Univ Autonoma Aguascalientes Ctr Ciencias Basicas Ave Univ 940Ciudad Univ Aguascalientes 20131 Ags Mexico Univ Autonoma Aguascalientes Dept Matemat & Fis Ave Univ 940Ciudad Univ Aguascalientes 20131 Ags Mexico

In this work, we introduce two novel methodologies to compute the envelope of superficial electromyography signals. Our methods are based on the detection of activation and deactivation patterns using a change-point approach on the variances of the sample. More concretely, an iterative algorithms is proposed to select the change-points between two segments of the signal based on some local statistics introduced in this work. The signal is split up into two segments, and a new search for change-points is recursively conducted in each subsequence. The change -points make possible to calculate local envelopes which reflect the shape of the signal without ignoring the activation and deactivation landmarks. Two methods are proposed in this work, and the improvements with respect to methodologies available in the literature are shown using both synthetic and real data. A thorough analysis of the techniques is performed to that end. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Electromyography signal Envelope binary segmentation Change-points

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Multiple-change-point detection for high dimensional time series via sparsified binary segmentation

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JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY 2015年第2期77卷 475-507页

作者： Cho, Haeran Fryzlewicz, Piotr Univ Bristol Bristol BS8 1TW Avon England Univ London London Sch Econ & Polit Sci London WC2A 2AE England

Time series segmentation, which is also known as multiple-change-point detection, is a well-established problem. However, few solutions have been designed specifically for high dimensional situations. Our interest is in segmenting the second-order structure of a high dimensional time series. In a generic step of a binary segmentation algorithm for multivariate time series, one natural solution is to combine cumulative sum statistics obtained from local periodograms and cross-periodograms of the components of the input time series. However, the standard maximum' and average' methods for doing so often fail in high dimensions when, for example, the change points are sparse across the panel or the cumulative sum statistics are spuriously large. We propose the sparsified binary segmentation algorithm which aggregates the cumulative sum statistics by adding only those that pass a certain threshold. This sparsifying' step reduces the influence of irrelevant noisy contributions, which is particularly beneficial in high dimensions. To show the consistency of sparsified binary segmentation, we introduce the multivariate locally stationary wavelet model for time series, which is a separate contribution of this work.

关键词： binary segmentation Cumulative sum statistic High dimensional time series Locally stationary wavelet model Multiple-change-point detection Thresholding

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WILD binary segmentation FOR MULTIPLE CHANGE-POINT DETECTION

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ANNALS OF STATISTICS 2014年第6期42卷 2243-2281页

作者： Fryzlewicz, Piotr London Sch Econ Dept Stat London WC2A 2AE England

We propose a new technique, called wild binary segmentation (WBS), for consistent estimation of the number and locations of multiple change-points in data. We assume that the number of change-points can increase to infinity with the sample size. Due to a certain random localisation mechanism, WBS works even for very short spacings between the change-points and/or very small jump magnitudes, unlike standard binary segmentation. On the other hand, despite its use of localisation, WBS does not require the choice of a window or span parameter, and does not lead to a significant increase in computational complexity. WBS is also easy to code. We propose two stopping criteria for WBS: one based on thresholding and the other based on what we term the 'strengthened Schwarz information criterion'. We provide default recommended values of the parameters of the procedure and show that it offers very good practical performance in comparison with the state of the art. The 'WBS methodology is implemented in the R package wbs, available on CRAN. In addition, we provide a new proof of consistency of binary segmentation with improved rates of convergence, as well as a corresponding result for WBS.

关键词： Multiple change-points change-point detection binary segmentation randomised algorithms thresholding Bayesian information criterion

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Human Locomotion Monitoring in Space Flight: Retrospective Nonparametric Changepoint Detection Methods

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MICROGRAVITY SCIENCE AND TECHNOLOGY 2025年第2期37卷 1-27页

作者： Shestoperov, A. I. Ivchenko, A. V. Fomina, E. V. Russian Acad Sci Keldysh Inst Appl Math Miusskaya Sq 4 Moscow 125047 Russia Russian Acad Sci Inst Biomed Problems State Sci Ctr Russian Federat Khoroshevskoe ShKhoroshevskoe ShKhoroshevskoe ShKh Moscow 123007 Russia

The paper is dedicated to the analysis of medico-biological data obtained during locomotor testing of astronauts. Accurate data interpretation plays a crucial role in locomotion system monitoring, prophylaxis of long-duration spaceflight negative effects and thus in the development of an autonomous medical support system for deep space expeditions. During the locomotor testing the astronaut changes motion modes in accordance with the prescribed training protocol while running on the treadmill, and data such as speed, support pressure, heart rate frequency, etc., are collected simultaneously. The astronaut may follow either an individual protocol developed by specialists or perform his personal protocol at every fourth day of the micro cycle. Our task is to identify unknown motion modes by the means of a posteriori time series segmentation and, specifically, in the presence of various transitional processes as well as signal loss periods. The presence of tricky profiles does not allow for preliminary hypotheses about the distribution pattern of the dataset under study. The article consists of two parts. Firstly, it provides a detailed overview of several modern retrospective (offline) nonparametric multiple changepoint detection methods in multidimensional time series. A change point means an abrupt change in the probability properties of the observed series occurring at an unknown time instant. When describing the algorithms, emphasis is placed on statistics as a measure of data homogeneity, numerical methods for solving optimization problems, and model selection methods. Secondly, the real speed profiles resulting from locomotor testing have been handled through the mentioned algorithms. The validation was performed on three characteristic experimental data samples, allowing for an assessment of the prospects of applying the described methods to the entire dataset.

关键词： Aerospace medicine Locomotion test Time series Changepoint segmentation Nonparametric Retrospective binary segmentation Dynamic programming Sliding window

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Transfer learning for surgical instrument segmentation in open surgery videos: a modified u-net approach with channel amplification

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SIGNAL IMAGE AND VIDEO PROCESSING 2024年第11期18卷 8061-8076页

作者： Bakiya, K. Savarimuthu, Nickolas Natl Inst Technol Dept Comp Applicat Tiruchirappalli Tamilnadu India

Minimally invasive surgeries reduce blood loss and quicker recovery times for patients compared to open surgeries. Equipped with high-definition 3D cameras, robotic surgical systems offer an enhanced visual perspective, empowering surgeons to make informed decisions and reduce damage to healthy tissues around the surgical site. At this juncture, emphasizing the importance of precise localization of surgical instruments in robotic-assisted surgeries is of utmost significance. The primary intention of this paper is to address the challenge of achieving accurate localization of surgical tools within registered tissue to establish clear marginal boundaries and prevent tissue tearing. So, surgeons can reduce the risk of human error with extremely precise and steady movements. In this paper, we proposed a binary segmentation model complemented with a customized U-Net architecture to ensure precise inference in surgical instrument segmentation. Additionally, the accuracy was enhanced through the incorporation of Red channel amplification and the Otsu thresholding approach. The effectiveness of our proposed Modified U-Net is validated through experiments conducted on both the publicly available surgical instrument segmentation dataset, MICCAI 2017 EndoVis Challenge (EndoVis2017), and Neuro surgical tools (NST) dataset consisting of both brain and spine tumor removal procedures. The results presented compelling evidence of the superior performance attained through the integration of the modified U-Net with Red channel amplification and Otsu thresholding with remarkable outcomes including 99.5% accuracy and a Dice score of 0.9846 in binary segmentation, 99.41% accuracy, and a Dice score of 0.9751 in part segmentation, and 99.35% accuracy and a Dice score of 0.9726 in type segmentation. We presented both quantitative and qualitative inference results for NST dataset samples. These results were obtained by leveraging a transfer learning approach, where the model was trained on

关键词： Surgical instrument segmentation binary segmentation Part segmentation Type segmentation Open surgery dataset Transfer learning Red channel amplification Otsu thresholding

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