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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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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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Comparative Assessment of SAMM and MONAI algorithms in Spleen Image segmentation: Approaches and Metrics 12

Comparative Assessment of SAMM and MONAI Algorithms in Splee...

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2024 IEEE Andescon Conference

作者： Paul Salvatierra-Guillermo, Diego Zavaleta Jave, Valeria Oriana Pontificia Univ Catolica Peru Fac Sci & Engn Lima Peru

ISBN: (纸本)9798350355291;9798350355284

This study presents a comparative assessment of two prominent open-source segmentation algorithms, SAMM (Segment Any Medical Model) and MONAI (Medical Open Network for AI), in the context of spleen image segmentation. While both algorithms leverage artificial intelligence for medical image analysis, they differ significantly in their design and performance. Using a dataset of 10 thoracic CT scans from the "Task09_Spleen" collection, we evaluated these algorithms on a laptop with standardized hardware specifications. The segmentation quality was assessed using three key metrics: Dice Similarity Index (DSC), Average Hausdorff Distance (AHD), and Volumetric Similarity (VS), all computed using the MISeval library. Our results consistently demonstrate MONAI's superior performance across all metrics, with mean scores of 0.956 for DSC, 6.258 for AHD, and 0.984 for VS, compared to SAMM's 0.719, 29.474, and 0.858 respectively. These findings not only highlight MONAI's enhanced precision and efficacy in spleen segmentation but also underscore its potential as a more reliable tool for medical image analysis in similar contexts.

关键词： SAMM MONAI Medical Images segmentation algorithms Dice Index Average Hausdorff Distance Volumetric Similarity

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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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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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algorithms and Complexity on Indexing Founder Graphs

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ALGORITHMICA 2023年第6期85卷 1586-1623页

作者： Equi, Massimo Norri, Tuukka Alanko, Jarno Cazaux, Bastien Tomescu, Alexandru, I Makinen, Veli Univ Helsinki Dept Comp Sci POB 68Pietari Kalmin Katu 5 Helsinki 00014 Finland Dalhousie Univ Fac Comp Sci POB 150006050 Univ Ave Halifax NS Canada Univ Montpellier CNRS LIRMM LIRMM UMR 5506 CC477161 Rue Ada F-34095 Montpellier 5 France

We study the problem of matching a string in a labeled graph. Previous research has shown that unless the Orthogonal Vectors Hypothesis (OVH) is false, one cannot solve this problem in strongly sub-quadratic time, nor index the graph in polynomial time to answer queries efficiently (Equi et al. ICALP 2019, SOFSEM 2021). These conditional lower-bounds cover even deterministic graphs with binary alphabet, but there naturally exist also graph classes that are easy to index: For example, Wheeler graphs (Gagie et al. Theor. Comp. Sci. 2017) cover graphs admitting a Burrows-Wheeler transform -based indexing scheme. However, it is NP-complete to recognize if a graph is a Wheeler graph (Gibney, Thankachan, ESA 2019). We propose an approach to alleviate the construction bottleneck of Wheeler graphs. Rather than starting from an arbitrary graph, we study graphs induced from multiple sequence alignments (MSAs). Elastic degenerate strings (Bernadini et al. SPIRE 2017, ICALP 2019) can be seen as such graphs, and we introduce here their generalization: elastic founder graphs. We first prove that even such induced graphs are hard to index under OVH. Then we introduce two subclasses, repeat-free and semi-repeat-free graphs, that are easy to index. We give a linear time algorithm to construct a repeat-free (non-elastic) founder graph from a gapless MSA, and (parameterized) near-linear time algorithms to construct a semi-repeat-free (repeat-free, respectively) elastic founder graph from general MSA. Finally, we show that repeat-free founder graphs admit a reduction to Wheeler graphs in polynomial time.

关键词： Graph algorithms Computational complexity Compressed data structures String matching Multiple sequence alignment segmentation algorithms Pangenomics

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A Novel Change Point Detection Approach for Analysis of Time-Ordered Satellite Imagery

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JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2023年第1期51卷 31-41页

作者： Yadav, Ashwin Jain, Kamal Pandey, Akshay Majumdar, Joydeep Indian Inst Technol Roorkee Dept Civil Engn Geomat Engn Roorkee 247667 Uttar Pradesh India Indian Inst Technol Roorkee Ctr Excellence Disaster Mitigat & Management Roorkee 247667 Uttar Pradesh India Indian Inst Technol Indore Dept Mech Engn Indore 452020 Madhya Pradesh India

Change detection through the analysis of images is a fundamental step in the remote sensing analysis framework. It is an emerging area of research with many methods and algorithms in place. Most of the work till date is based on applied change detection methods to a pair of images in the case of satellite imagery. This paper proposes a novel change point detection methodology for a time-ordered set of images which shall enhance the efficiency within the overall image processing framework. It focuses on change detection applied to the set of time-ordered images to identify the exact pair of bi-temporal images about the change point, thereby being of great value to the image analyst in the overall image processing workflow. The paper proposes a metric to detect changes in time-ordered image series in the form of rank ordered threshold values extracted post-application of the segmentation algorithms to the bi-temporal image pair differences derived from time-ordered images. The rank of the threshold value specific to the image pair indicates the relevance and quantum of changes in the image pair among the entire time-ordered image series. A higher rank of threshold conforms to the pair of images with higher image changes. A total of four segmentation algorithms including the well-known Otsu, minimum cross-entropy method by Li et al. methods have been applied to a set of ten time-ordered satellite image sequences as part of the study. Li's cross-entropy method is found to provide the best results for the determination of the change point. Such a change point detection methodology is applicable not only to satellite imagery but also to a general time-ordered set of images and video frames.

关键词： Change point detection Time-ordered images Satellite imagery Difference image Remote sensing Threshold values segmentation algorithms

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CoSeNet: A novel approach for optimal segmentation of correlation matrices

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DIGITAL SIGNAL PROCESSING 2024年 144卷

作者： Palomo-Alonso, A. Casillas-Perez, D. Jimenez-Fernandez, S. Portilla-Figueras, A. Salcedo-Sanz, S. Ctra Madrid Barcelona Dept Signal Proc & Commun Km 33 Alcala De Henares 28805 Madrid Spain Dept Signal Proc & Commun Camino Molino 5 Fuenlabrada 28942 Madrid Spain

In this paper, we propose a novel approach for the optimal identification of correlated segments in noisy correlation matrices. The proposed model is known as CoSeNet (Correlation segmentation Network) and is based on a four-layer algorithmic architecture that includes several processing layers: input, formatting, re-scaling, and segmentation layer. The proposed model can effectively identify correlated segments in such matrices, better than previous approaches for similar problems. Internally, the proposed model utilizes an overlapping technique and uses pre-trained Machine Learning (ML) algorithms, which makes it robust and generalizable. CoSeNet approach also includes a method that optimizes the parameters of the re-scaling layer using a heuristic algorithm and fitness based on a Window Difference-based metric. The output of the model is a binary noise-free matrix representing optimal segmentation as well as its segmentation points and can be used in a variety of applications, obtaining compromise solutions between efficiency, memory, and speed of the proposed deployment model.

关键词： Correlation matrices segmentation algorithms Multi-algorithm architecture Metaheuristic optimization Machine learning

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RETHINKING COMPUTER-AIDED PELVIS segmentation 47

RETHINKING COMPUTER-AIDED PELVIS SEGMENTATION

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47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Yuan, Siming Liu, Qing Liao, Shenghui Han, Fuchang Wei, Haitao Zhang, Yingqi Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Cent South E3D Digital Med & Virtual Real Res Ctr Changsha 410083 Peoples R China Tongji Univ Tongji Hosp Sch Med Shanghai 200000 Peoples R China

ISBN: (纸本)9781665405409

As an important structure connecting the spine and lower limbs, the abnormal pelvis is one of the threats to human health worldwide, leading to millions of deaths every year. Although early diagnosis and treatment can greatly improve the chances of survival, it remains a major challenge, especially for pelvis fractures. Computer-aided pelvis segmentation (CPS) is a promising choice for the abnormal pelvis due to the great success of deep learning. However, when it comes to the abnormal pelvis diagnosis, the lack of training data has hampered the progress of CPS. To solve this problem, we have published a large-scale pelvis dataset, namely the Pelvis Computed tomography (CT) image (PCT14K) dataset. This dataset contains 14487 CT slices with the corresponding label for pelvis areas, while the existing largest public pelvis dataset part comes from existing data sets related to other organs with a lot of redundant information, and another part comes from the orthopedic hospital without a corresponding label. The proposed dataset enables the training of sophisticated segmentation networks for highquality CPS. Some mainstream segmentation algorithms are trained and evaluated on the proposed PCT14K dataset and served as the baselines for future research. The published dataset will be available at https://***/YUAN-SIMING/PCT14K.

关键词： Pelvis deep learning CT images segmentation algorithms

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