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segmentation of prostate ultrasound images: the state of the art and the future directions of segmentation algorithms

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ARTIFICIAL INTELLIGENCE REVIEW 2023年第1期56卷 615-651页

作者： Jiang, Jingang Guo, Yafeng Bi, Zhuming Huang, Zhiyuan Yu, Guang Wang, Jinke Harbin Univ Sci & Technol Key Lab Adv Mfg & Intelligent Technol Minist Educ Harbin 150080 Heilongjiang Peoples R China Purdue Univ Dept Civil & Mech Engn Ft Wayne IN 46805 USA Harbin Inst Technol State Key Lab Robot & Syst Harbin 150001 Heilongjiang Peoples R China Tsinghua Univ Dept Mech Engn Beijing 100084 Peoples R China

Nowadays, prostate cancer has surpassed lung cancer as the most common type of cancer, segmentation of prostate ultrasound images is a critical step in the detection and planning treatment of prostate cancer. However, both ultrasound imaging characteristics and the physiology of the prostate make it difficult to determine the prostate boundaries in ultrasound images. In this paper, we provide a systematic review of advances in the field of ultrasound prostate image segmentation. In particular, three categories of algorithms are reviewed and compared, including edge-based segmentation, region-based segmentation, and those based on specific theoretical models. To understand the state of the art of different segmentations of the prostate ultrasound images, we conduct a literature analysis and a series of comparisons between different algorithms. The features and limitations of each category of segmentation algorithms are further discussed. Finally, we identified promising research directions in advancing the segmentation algorithms for the processing of ultrasound prostate images.

关键词： Prostate cancer Ultrasound images Image segmentation segmentation algorithms Diagnosis of prostate cancer Citespace

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The determination of optimum segmentation parameters using genetic algorithms: Application to different segmentation algorithms and transmission electron microscopy tomography reconstructed volumes

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MICROSCOPY RESEARCH AND TECHNIQUE 2023年第10期86卷 1237-1248页

作者： Martinez, Roberto Fernandez Okariz, Ana Iturrondobeitia, Maider Ibarretxe, Julen Univ Basque Country UPV EHU Coll Engn Bilbao Dept Elect Engn Alameda Urquijo s-n Bilbao 48013 Spain Univ Basque Country UPV EHU Coll Engn Bilbao Dept Appl Phys Bilbao Spain Univ Basque Country UPV EHU Coll Engn Bilbao Graph Design & Project Engn Dept Bilbao Spain

A method for optimizing an automatic selection of values for parameters that feed segmentation algorithms is proposed. Evolutionary optimization techniques in combination with a fitness function based on a mutual information parameter have been used to find the optimal parameter values of region growing, fuzzy c-means and graph cut segmentation algorithms. To validate the method, the segmentation of two transmission electron microscopy tomography reconstructed volumes of a carbon black-reinforced rubber and a polylactic acid and clay nanocomposite is carried out (i) using evolutionary optimization techniques and (ii) manually by experts. The results confirm that the use of evolutionary optimization techniques, such as genetic algorithms, reduces the computational operation cost needed for a total grid search of segmentation parameters, reducing the probability of reaching a false optimum, and improving the segmentation quality. HighlightsA new approach to optimize 3D segmentation *** to optimize segmentation parameters and improve segmentation *** on the results when using region growing, fuzzy c-means and graph cuts algorithms.

关键词： characterization genetic algorithms microscopy mutual information optimization techniques segmentation segmentation algorithms TEM tomography

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Comparative Testing of DNA segmentation algorithms Using Benchmark Simulations

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MOLECULAR BIOLOGY AND EVOLUTION 2010年第5期27卷 1015-1024页

作者： Elhaik, Eran Graur, Dan Josic, Kresimir Univ Houston Dept Biol & Biochem Houston TX 77004 USA Univ Houston Dept Math Houston TX 77004 USA

Numerous segmentation methods for the detection of compositionally homogeneous domains within genomic sequences have been proposed. Unfortunately, these methods yield inconsistent results. Here, we present a benchmark consisting of two sets of simulated genomic sequences for testing the performances of segmentation algorithms. Sequences in the first set are composed of fixed-sized homogeneous domains, distinct in their between-domain guanine and cytosine (GC) content variability. The sequences in the second set are composed of a mosaic of many short domains and a few long ones, distinguished by sharp GC content boundaries between neighboring domains. We use these sets to test the performance of seven segmentation algorithms in the literature. Our results show that recursive segmentation algorithms based on the Jensen-Shannon divergence outperform all other algorithms. However, even these algorithms perform poorly in certain instances because of the arbitrary choice of a segmentation-stopping criterion.

关键词： isochores GC content segmentation algorithms Jensen-Shannon divergence statistic entropy genome composition benchmark simulations

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Evaluating segmentation algorithms for diffusion-weighted MR images: a task-based approach

Evaluating segmentation algorithms for diffusion-weighted MR...

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Conference on Image Perception, Observer Performance, and Technology Assessment

作者： Jha, Abhinav K. Kupinski, Matthew A. Rodriguez, Jeffrey J. Stephen, Renu M. Stopeck, Alison T. Univ Arizona Coll Opt Sci Tucson AZ 85721 USA Univ Arizona Dept Elect & Comp Engn Tucson AZ USA Univ Arizona Arizona Canc Ctr Tucson AZ USA

ISBN: (纸本)9780819480286

Apparent Diffusion Coefficient (ADC) of lesions obtained from Diffusion Weighted Magnetic Resonance Imaging is an emerging biomarker for evaluating anti-cancer therapy response. To compute the lesion's ADC, accurate lesion segmentation must be performed. To quantitatively compare these lesion segmentation algorithms, standard methods are used currently. However, the end task from these images is accurate ADC estimation, and these standard methods don't evaluate the segmentation algorithms on this task-based measure. Moreover, standard methods rely on the highly unlikely scenario of there being perfectly manually segmented lesions. In this paper, we present two methods for quantitatively comparing segmentation algorithms on the above task-based measure;the first method compares them given good manual segmentations from a radiologist, the second compares them even in absence of good manual segmentations.

关键词： Task-based quantitative evaluation segmentation algorithms no-gold-standard

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PERFORMANCE EVALUATION OF TWO HIGH PROFILE segmentation algorithms IN IRIS RECOGNITION SYSTEM

PERFORMANCE EVALUATION OF TWO HIGH PROFILE SEGMENTATION ALGO...

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作者： ADEPOJU, SUNDAY FEMI AHMADU BELLO UNIVERSITY

学位级别：硕士

Iris is an effective biometric application of an individual for security-related applications. However, the iris segmentation process is challenging due to the presence of eye lashes that occlude the iris. Iris segmentation is an important phase in the whole iris recognition system, for it determines the accuracy of matching. To find a fast, effective and exact iris segmentation algorithm is the key step of iris recognition. This thesis compares the two high profile segmentation algorithms of Daugman (integro-differential) and Wildes (circular hough). Simulating the two algorithms with MATLAB 2012 and image datasets from Chinese Academy of Science Institute of Automation (CASIA) version 4, the evaluation of the algorithms was carried out using the performance metric False Acceptance Rate (FAR), False Rejection Rate (FRR) and Recognition Accuracy. The analysis of the result, indicated that the Circular Hough is more accurate than intego-differential as it shows higher recognition accuracy and lower error rate.

关键词： PERFORMANCE EVALUATION HIGH PROFILE segmentation algorithms IRIS RECOGNITION SYSTEM

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segmentation algorithms of Chromosome Images

Segmentation Algorithms of Chromosome Images

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International Conference on Computer Science and Network Technology

作者： Wenzhong Yan Dongming Li Dept. of Comput. North China Inst. of Sci. & Technol. Beijing China

ISBN: (纸本)9781479905607

The chromosome segmentation is the most important step in the automatic chromosome analysis system. In this paper, a few basic chromosome segmentation algorithms are investigated firstly. As a common phenomenon, the partial touching and overlapping chromosomes occur in almost every metaphase images. So, as a difficult yet vital problem, the algorithms for overlapping chromosomes are discussed in detail. The principle and the realization of these algorithms are analyzed. Results of these algorithms are compared and discussed.

关键词： biology computing cellular biophysics image segmentation automatic chromosome analysis system chromosome images chromosome segmentation algorithm metaphase image overlapping chromosomes partial touching chromosomes segmentation algorithms Accuracy Algorithm design and analysis Biological cells Classification algorithms Entropy Image segmentation Shape chromosome overlapping segmentation image segmentation Algorithm design and analysis Chromosomes biology computing cellular biophysics cytology Sorting algorithm segmentation segmentation entropy

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Study on Individual Tree segmentation of Different Tree Species Using Different segmentation algorithms Based on 3D UAV Data

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FORESTS 2023年第7期14卷 1327-1327页

作者： Liu, Yao You, Haotian Tang, Xu You, Qixu Huang, Yuanwei Chen, Jianjun Guilin Univ Technol Coll Geomat & Geoinformat 12 Jiangan Rd Guilin 541006 Peoples R China Guilin Univ Technol Guangxi Key Lab Spatial Informat & Geomat 12 Jiangan Rd Guilin 541004 Peoples R China

Individual structural parameters of trees, such as forest stand tree height and biomass, serve as the foundation for monitoring of dynamic changes in forest resources. Individual tree structural parameters are closely related to individual tree crown segmentation. Although three-dimensional (3D) data have been successfully used to determine individual tree crown segmentation, this phenomenon is influenced by various factors, such as the (i) source of 3D data, (ii) the segmentation algorithm, and (iii) the tree species. To further quantify the effect of various factors on individual tree crown segmentation, light detection and ranging (LiDAR) data and image-derived points were obtained by unmanned aerial vehicles (UAVs). Three different segmentation algorithms (PointNet++, Li2012, and layer-stacking segmentation (LSS)) were used to segment individual tree crowns for four different tree species. The results show that for two 3D data, the crown segmentation accuracy of LiDAR data was generally better than that obtained using image-derived 3D data, with a maximum difference of 0.13 in F values. For the three segmentation algorithms, the individual tree crown segmentation accuracy of the PointNet++ algorithm was the best, with an F value of 0.91, whereas the result of the LSS algorithm yields the worst result, with an F value of 0.86. Among the four tested tree species, the individual tree crown segmentation of Liriodendron chinense was the best, followed by Magnolia grandiflora and Osmanthus fragrans, whereas the individual tree crown segmentation of Ficus microcarpa was the worst. Similar crown segmentation of individual Liriodendron chinense and Magnolia grandiflora trees was observed based on LiDAR data and image-derived 3D data. The crown segmentation of individual Osmanthus fragrans and Ficus microcarpa trees was superior according to LiDAR data to that determined according to image-derived 3D data. These results demonstrate that the source of 3D data, the segmenta

关键词： image-derived points LiDAR data segmentation algorithms individual tree tree species

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A Review of Advanced Deep Learning Methods of Multi-Target segmentation for Breast Cancer WSIs

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IEEE ACCESS 2025年 13卷 76016-76037页

作者： Xu, Qiaoyi Adam, Afzan Abdullah, Azizi Bariyah, Nurkhairul Univ Kebangsaan Malaysia Fac Informat Sci & Technol Ctr Artificial Intelligence Technol Bangi 43600 Selangor Malaysia Zibo Vocat Inst Coll Intelligent Mfg Zibo 255314 Peoples R China Univ Kebangsaan Malaysia Fac Med Dept Pathol Kuala Lumpur 56000 Malaysia

Breast cancer is one of the most common cancers among women, with its heterogeneity posing significant challenges for diagnosis and treatment, profoundly impacting patient prognosis and quality of life. Whole Slide Imaging (WSI) in digital pathology provides high-resolution images that enable a comprehensive examination of the tumor microenvironment, offering advanced tools for breast cancer diagnosis and prognostic evaluation. However, manually reviewing whole slide images (WSIs) for tissue segmentation is time-consuming and prone to errors, highlighting the need for multi-target deep learning models to automate the segmentation of these complex structures. Multi-target segmentation offers distinct advantages by simultaneously processing multiple interrelated tissue regions within a single image, thereby enhancing accuracy and efficiency. Despite the potential of deep learning techniques in automating pathological analysis, their clinical adoption faces significant challenges. To address these, this paper proposes six criteria focused on clinical acceptability of deep learning methods: inherent limitations of WSIs, feature extraction, annotation requirements, efficiency, automated quantification, and interpretability. A rigorous review of publicly available datasets and deep learning methods identifies key challenges for clinical adoption. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this review analyzes 29 core articles, highlighting the critical role of multi-target segmentation in breast cancer digital pathology while assessing the limitations of these techniques in clinical applications. Based on this analysis, this paper proposes six criteria to enhance the diagnostic performance of deep learning methods in multi-target segmentation for breast cancer digital pathology and to improve the clinical acceptability of deep learning methods.

关键词： Annotations Breast cancer Pathology Deep learning Image segmentation Cancer Accuracy Image resolution Feature extraction XML multi-target segmentation digital pathology whole slide images segmentation algorithms deep learning

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Efficient algorithms for segmentation of item-set time series

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DATA MINING AND KNOWLEDGE DISCOVERY 2008年第3期17卷 377-401页

作者： Chundi, Parvathi Rosenkrantz, Daniel J. Univ Nebraska Dept Comp Sci Omaha NE 68106 USA SUNY Albany Dept Comp Sci Albany NY 12222 USA

We propose a special type of time series, which we call an item-set time series, to facilitate the temporal analysis of software version histories, email logs, stock market data, etc. In an item-set time series, each observed data value is a set of discrete items. We formalize the concept of an item-set time series and present efficient algorithms for segmenting a given item-set time series. segmentation of a time series partitions the time series into a sequence of segments where each segment is constructed by combining consecutive time points of the time series. Each segment is associated with an item set that is computed from the item sets of the time points in that segment, using a function which we call a measure function. We then define a concept called the segment difference, which measures the difference between the item set of a segment and the item sets of the time points in that segment. The segment difference values are required to construct an optimal segmentation of the time series. We describe novel and efficient algorithms to compute segment difference values for each of the measure functions described in the paper. We outline a dynamic programming based scheme to construct an optimal segmentation of the given item-set time series. We use the item-set time series segmentation techniques to analyze the temporal content of three different data sets-Enron email, stock market data, and a synthetic data set. The experimental results show that an optimal segmentation of item-set time series data captures much more temporal content than a segmentation constructed based on the number of time points in each segment, without examining the item set data at the time points, and can be used to analyze different types of temporal data.

关键词： Item-set time series Measure function Segment difference segmentation algorithms Optimal segmentation

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Comparative study of algorithms for ECG segmentation

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2017年第0期34卷 166-173页

作者： Beraza, Idoia Romero, Inaki IMEC Holst Ctr Body Area Networks Eindhoven Netherlands

Accurate automatic identification of fiducial points within an ECG is required for the automatic interpretation of this signal. Several methods exist in the literature for automatic ECG segmentation. These algorithms are based on different methodologies and often evaluated with different datasets and protocols, which makes their performance challenging to compare. For this study, nine segmentation algorithms were selected from the literature and evaluated with the same protocol in order to study their performance. One hundred signals from the PhysioNet's QT database were used for this evaluation. Results showed that one of the algorithms based in the discrete wavelet transform achieved sensitivity of 100% when detecting the onset and offset of the QRS complex. An algorithm using the Multi-scale Morphological Derivate achieved sensitivities of 99.81%, 98.17% and 99.56% when detecting the peak, onset and offset respectively of the P-wave. When segmenting the T-wave, an algorithm based on the Phasor transform had a good performance with sensitivities of 97.78%, 97.81% and 95.43% when detecting the peak, onset and offset, respectively. Additionally, probabilistic methods such as Hidden Markov Models had good results due to the fact that they can learn from real signals and adapt to specific conditions. However, these techniques are often computationally more complex and require training. This study could help in selecting optimal algorithms for ECG segmentation when implementing a system for automatic ECG interpretation. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： ECG segmentation algorithms Fiducial point's detection

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