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A systematic review of retinal fundus image segmentation and classification methods using convolutional neural networks

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Healthcare Analytics 2023年 4卷 100261-100261页

作者： Ilesanmi, Ademola E. Ilesanmi, Taiwo Gbotoso, Gbenga A. University of Pennsylvania 3710 Hamilton Philadelphia 19104 PA United States National Population Commission Abuja Nigeria Lagos State University of Science and Technology Ikorodu Nigeria

Retinal fundus images play a crucial role in the early detection of eye problems, aiding in timely diagnosis and treatment to prevent vision loss or blindness. With advancements in technology, Convolutional Neural Network (CNN) algorithms have emerged as effective tools for recognition, delineation, and classification tasks. This study proposes a comprehensive review of CNN algorithms used for retinal fundus image segmentation and classification. Our review follows a systematic approach, exploring diverse repositories to identify studies employing CNN to segment and classify retinal fundus images. Utilizing CNNs in the segmentation and classification of retinal fundus images can enhance the precision of segmentation outcomes and alleviate the sole dependence on human experts. This approach enables more accurate segmentation results, reducing the burden on human experts. A total of sixty-two studies are included in our review, analyzing aspects such as database usage and the advantages and disadvantages of the methods employed. The review provides valuable insights, limitations, observations, and future directions in the field. Despite certain limitations, the findings indicate that CNN algorithms consistently achieve high accuracies. The comprehensive examination of the included studies sheds light on the potential of CNN in retinal fundus image analysis. © 2023 The Author(s)

关键词： Computer-aided image detection Convolutional neural network Eye-related disorders image segmentation and classification Retinal disease Retinal fundus

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Finding a Suitable Class Distribution for Building Histological images Datasets Used in Deep Model Training-The Case of Cancer Detection

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JOURNAL OF DIGITAL IMAGING 2022年第5期35卷 1326-1349页

作者： Reshma, Ismat Ara Franchet, Camille Gaspard, Margot Ionescu, Radu Tudor Mothe, Josiane Cussat-Blanc, Sylvain Luga, Herve Brousset, Pierre Univ Toulouse CNRS UMR5505 IRIT Toulouse France Univ Canc Inst Toulouse Oncopole Dept Pathol Toulouse France Univ Bucharest Bucharest Romania Artificial & Nat Intelligence Toulouse Inst Toulouse France Univ Toulouse III Paul Sabatier CNRS INSERM UMR Canc Res Ctr Toulouse CRCT 1037ERL 5294 Toulouse France Lab Excellence TOUCAN Toulouse France

The class distribution of a training dataset is an important factor which influences the performance of a deep learning-based system. Understanding the optimal class distribution is therefore crucial when building a new training set which may be costly to annotate. This is the case for histological images used in cancer diagnosis where image annotation requires domain experts. In this paper, we tackle the problem of finding the optimal class distribution of a training set to be able to train an optimal model that detects cancer in histological images. We formulate several hypotheses which are then tested in scores of experiments with hundreds of trials. The experiments have been designed to account for both segmentation and classification frameworks with various class distributions in the training set, such as natural, balanced, over-represented cancer, and over-represented non-cancer. In the case of cancer detection, the experiments show several important results: (a) the natural class distribution produces more accurate results than the artificially generated balanced distribution;(b) the over-representation of non-cancer/negative classes (healthy tissue and/or background classes) compared to cancer/positive classes reduces the number of samples which are falsely predicted as cancer (false positive);(c) the least expensive to annotate non-ROI (non-region-of-interest) data can be useful in compensating for the performance loss in the system due to a shortage of expensive to annotate ROI data;(d) the multi-label examples are more useful than the single-label ones to train a segmentation model;and (e) when the classification model is tuned with a balanced validation set, it is less affected than the segmentation model by the class distribution of the training set.

关键词： Computer-aided diagnosis Medical information retrieval image segmentation and classification Deep learning Class-biased training Class distribution analysis Histological image

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A framework of computer vision-enhanced microfluidic approach for automated assessment of the transient sickling kinetics in sickle red blood cells

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FRONTIERS IN PHYSICS 2024年 12卷 1331047页

作者： Qiang, Yuhao Xu, Mengjia Pochron, Mira Patel Jupelli, Madhulika Dao, Ming MIT Dept Mat Sci & Engn Cambridge MA 02139 USA New Jersey Inst Technol Ying Wu Coll Comp Dept Data Sci Newark NJ USA MIT Ctr Brains Minds & Machines Cambridge MA USA Pfizer Inc New York NY USA

The occurrence of vaso-occlusive crisis greatly depends on the competition between the sickling delay time and the transit time of individual sickle cells, i.e., red blood cells from sickle cell disease (SCD) patients, while they are traversing the circulatory system. Many drugs for treating SCD work by inhibiting the polymerization of sickle hemoglobin (HbS), effectively delaying the sickling process in sickle cells (SS RBCs). Most previous studies on screening anti-sickling drugs, such as voxelotor, rely on in vitro testing of sickling characteristics, often conducted under prolonged deoxygenation for up to 1 hour. However, since the microcirculation of RBCs typically takes less than 1 minute, the results of these studies may be less accurate and less relevant for in vitro-in vivo correlation. In our current study, we introduce a computer vision-enhanced microfluidic framework designed to automatically capture the transient sickling kinetics of SS RBCs within a 1-min timeframe. Our study has successfully detected differences in the transient sickling kinetics between vehicle control and voxelotor-treated SS RBCs. This approach has the potential for broader applications in screening anti-sickling therapies.

关键词： deep learning microfluidics image segmentation and classification drug testing sickle cell disease automated sickling kinetics assay

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An intelligent and automated 3D surface defect detection system for quantitative 3D estimation and feature classification of material surface defects

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OPTICS AND LASERS IN ENGINEERING 2021年 144卷 106633-106633页

作者： Zong, Yulong Liang, Jin Wang, Huan Ren, Maodong Zhang, Mingkai Li, Wenpan Lu, Wang Ye, Meitu Xi An Jiao Tong Univ Sch Mech Engn 28 Xianning West Rd Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 71054 Shaanxi Peoples R China Xi An Jiao Tong Univ Inst Sci & Educ Dev Xian 710049 Shaanxi Peoples R China XTOP 3D Technol Shenzhen Co Ltd Innovat Lab Shenzhen 518060 Peoples R China

To evaluate defects on the surface of the materials at the 3D level accurately and quantitatively, a 3D surface defect detection system based on stereo vision is presented, which can extract the precise 3D defect features of the detected object. The proposed detection system consists of two image capture modules and a turntable to capture the complete 3D information and color texture information from the object surface. More precisely, each image capture module is a binocular stereo vision system containing two monochrome cameras, a color camera, and a speckle projector which is used to reconstruct the 3D point clouds of the object surface based on stereo digital image correlation (stereo-DIC). Furthermore, a point-image mapping relationship between the reconstructed 3D object points and the color images is established. Eventually, the 3D characteristic parameters of defects are calculated by the corresponding 3D point cloud of the defect area obtained by segmenting the defect area using the image segmentation and point cloud segmentation algorithms according to this point-image mapping relationship. A convolutional neural network named DenseNets is employed to identify defect types intelligently. A high-precision mull-camera calibration method based on close-range photogrammetry is applied to ensure system detection accuracy in the proposed system. The experimental results demonstrate that the system has higher accuracy and better performance in system calibration, 3D reconstruction, and defect feature calculation.

关键词： 3D defect detection system 3D reconstruction Point-image relationship mapping image segmentation and classification Point cloud segmentation 3D feature calculation

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An Algorithm for Various Crop Diseases Detection and classification using Leaves images

An Algorithm for Various Crop Diseases Detection and Classif...

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International Conference on Data, Engineering and Applications (IDEA)

作者： Yashwant Kurmi Suchi Gangwar Dheeraj Agrawal Satrughan Kumar Deepratna Saxena Moly Saxena Harishanker Shrivastava Maulana Azad National Institute of Technology Bhopal India Ram Krishna Dharmarth Foundation University Bhopal India Rafi Ahmad Kidwai College of Agriculture Sehore India

ISBN: (数字)9781728157184

ISBN: (纸本)9781728157191

Economic graph of the most of the developing countries depends on the agricultural production. To maintain the economical figure the crop damaging factors that affects yield as various diseases needs to be diagnosed and cured appropriately. To determine the crop disease based on the leaf image analysis the proposed technique uses local image information. The local information of leaf image can be extracted using the leaf color feature for seed region extraction and region growing along with the contouring through the level-set and sample consensus to fit the leaf boundary. The classification is performed using the Fisher vectors of the localized images. The effectiveness of this proposal is analyzed with PlantVillage dataset using the vector support machine and multi-layer perceptron model. The analysis shows that the proposed technique is better as compared to the state of art methods with average classification accuracy and area under curve of 91.22 % and 92 % respectively.

关键词： image segmentation and classification Computer-aided diagnosis image analysis Diagnosis, Computer-Assisted image analysis Plant health images Taxonomy foliage image information Area Under Curve state of the art

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Local Search-Based GeneSIS algorithm for the segmentation and classification of Remote-Sensing images

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2016年第4期9卷 1470-1492页

作者： Mylonas, Stelios K. Stavrakoudis, Dimitris G. Theocharis, John B. Zalidis, George C. Gitas, Ioannis Z. Aristotle Univ Thessaloniki Dept Elect & Comp Engn Thessaloniki 54124 Greece Aristotle Univ Thessaloniki Sch Forestry & Nat Environm Lab Forest Management & Remote Sensing Thessaloniki 54124 Greece Aristotle Univ Thessaloniki Fac Agr Thessaloniki 54124 Greece

A local search-based version of the so-called genetic sequential image segmentation (GeneSIS) algorithm is presented in this paper, for the classification of remotely sensed images. The new method combines the properties of the GeneSIS framework with the principles of the region growing segmentation algorithms. Localized GeneSIS operates on a fine-segmented image obtained after preliminary watershed transformation. segmentation proceeds by iterative expansions emanating from object cores, i.e., connected components of marked watersheds. At each expansion trial, the process involves three successively performed operations: 1) generation of the object's neighborhood to a specified order;2) local exploration of the neighborhood through an evolutionary algorithm to identify the best expansion to be merged;and 3) rearrangement of the object neighborhoods. We propose two priority strategies for the selection of the objects to be expanded and two different modes of operation performing either supervised or semisupervised segmentation of the image. The combination of the priority strategies and segmentation modes lead to four different implementations of localized GeneSIS. Due to the local search approach adopted here, the resulting algorithms have considerably lower execution times, while at the same time, they provide comparable classification accuracies compared to those produced by previous GeneSIS variants. Experimental analysis is conducted using a hyperspectral forest image, a multispectral agricultural image, and the Pavia Centre image over an urban area. Comparative results are also provided with existing segmentation algorithms.

关键词： Genetic algorithms (GAs) image segmentation and classification priority strategies region growing algorithms remote-sensing images watershed segmentation

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Forest Mapping Through Object-based image Analysis of Multispectral and LiDAR Aerial Data

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EUROPEAN JOURNAL OF REMOTE SENSING 2014年第1期47卷 117-131页

作者： Machala, Martin Zejdova, Lucie Mendel Univ Brno Fac Forestry & Wood Technol Dept Geoinformat Technol Brno 61300 Czech Republic

The objective of this research was to establish a set of rules for using Object-based image Analysis (OBIA) to semi-automatically map a given densely forested area. Aerial images (RGB and NIR bands) and LiDAR elevation data provided the raw data and were analysed using eCognition Developer 8 software. All the non-forested areas, including built-up areas, water surfaces or agricultural land, were identified first. The forested areas were then classified as stands composed principally of broadleaf trees, coniferous trees, mixed forest or clear-cuts, which was achieved with an accuracy of almost 90%. Subsequently the stands were classified on the basis of height, using 5 metres intervals, and this was achieved with an accuracy of just over 70%.

关键词： Object-Based image Analysis (OBIA) eCognition Developer image segmentation and classification forest division mapping LiDAR

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Prediction of total cerebral tissue volumes in normal appearing brain from sub-sampled segmentation volumes

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MAGNETIC RESONANCE IMAGING 2003年第9期21卷 977-982页

作者： Glass, JO Ji, Q Glas, LS Reddick, WE St Jude Childrens Res Hosp Dept Diagnost Imaging Memphis TN 38105 USA Univ Memphis Dept Elect & Comp Engn Memphis TN 38152 USA Univ Memphis Dept Biomed Engn Memphis TN 38152 USA

The need for anatomical coverage and multi-spectral information must be balanced against examination and processing time to ensure high-quality, feasible imaging protocols for clinical research of cerebral development in normal-appearing brains. The focus of this study was to create and assess models to estimate total cerebral volumes of gray matter, white matter, and cerebrospinal fluid (CSF) from anatomically defined sub-samples of full clinical examinations. Pediatric patients (18F, 11M;aged 1.7 to 18.7, median 5.2 years) underwent a clinical imaging protocol consisting of 3 mm contiguous T1-, T2-, PD-, and FLAIR-weighted images after obtaining informed consent. Magnetic resonance imaging (MRI) sets were registered, RF-corrected, and then analyzed with a hybrid neural network segmentation and classification algorithm to identify normal brain parenchyma. The correlation between the image subsets and the total cerebral volumes of gray matter, white matter and CSF were examined through linear regression analyses. Five sub-sampled sets were defined and assessed in each patient to produce estimation models which were all significantly correlated (p < 0.001) with the total cerebral volumes of gray matter, white matter, and CSF. Volumes were estimated from as little as a single representative slice requiring minimal processing time, 27 min, but with an average estimation error of approximately 6%. Larger sub-samples of approximately three-quarters of the full cerebral volume required much more processing time, 2 h and 4 min, but produced estimates with an average error less than 2%. This study demonstrated that investigators can choose the amount of cerebrum sampled to optimize the acquisition and processing time against the degree of accuracy needed in the total cerebral volume estimates. (C) 2003 Elsevier Inc. All rights reserved.

关键词： image segmentation and classification image segmentation and feature extraction neural networks volumetric analysis magnetic resonance imaging

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Quantitative MR assessment of longitudinal parenchyma changes in children treated for medulloblastoma

Quantitative MR assessment of longitudinal parenchyma change...

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Medical Imaging 2002 Conference

作者： Reddick, WE Glass, JO Wu, SJ Palmer, SL Mulhern, RK Gajjar, A St Jude Childrens Res Hosp Dept Diagnost Imaging Memphis TN 38105 USA

ISBN: (纸本)0819444294

Our research builds on the hypothesis that white matter damage, in children treated for cancer with cranial spinal irradiation, spans a continuum of severity that can be reliably probed using non-invasive MR technology and results in potentially debilitating neurological and neuropsychological problems. This longitudinal project focuses on 341 quantitative volumetric MR examinations from 58 children treated for medulloblastoma (MB) with cranial irradiation (CRT) of 35-40 Gy. Quadratic mixed effects models were used to fit changes in tissue volumes (white matter, gray matter, CSF, and cerebral) with time since CRT and age at CRT as covariates. We successfully defined algorithms that are useful in the prediction of brain development among children treated for MB.

关键词： quantitative MR neurotoxicity radiation therapy image processing image segmentation and classification

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segmentation and classification of normal appearing brain - how much is enough?

Segmentation and classification of normal appearing brain - ...

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Medical Imaging 2002 Conference

作者： Glass, JO Reddick, WE Ji, Q Glas, LS St Jude Childrens Res Hosp Dept Diagnost Imaging Memphis TN 38105 USA

ISBN: (纸本)0819444294

In this study, subsets of MR slices were examined to assess their ability to optimally predict the total cerebral volume of gray matter, white matter and CSF. Patients underwent a clinical imaging protocol consisting of T1-, T2-, PD-, and FLAIR-weighted images after obtaining informed consent. MR imaging sets were registered, RF-corrected, and then analyzed with a hybrid neural network segmentation and classification algorithm to identify normal brain parenchyma. After processing the data, the correlation between the image subsets and the total cerebral volumes of gray matter, white matter and CSF were examined. The 29 subjects (18F, 11 M) assessed in this study were 1.7-18.7 (median = 5.2) years of age. The five subsets accounted for 5%, 15%, 24%, 56%, and 79% of the total cerebral volume. The predictive correlation for gray matter, white matter, and CSF in each of these subsets were: 5% (R=0.94, 0.92, 0.91), 15% (R=0.93, 0.95, 0.94), 24% (R= 0.92, 0.95, 0.94), 56% (R=0.75, 0.95, 0.89), and 79% (R=0.89, 0.98, 0.99) respectively. All subsets of slices examined were significantly correlated (p<0.001) with the total cerebral volume of gray matter, white matter, and CSF.

关键词： image segmentation and classification image segmentation and feature extraction neural networks volumetric analysis magnetic resonance Imaging

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