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semi-automated segmentation of magnetic resonance images for thigh skeletal muscle and fat using threshold technique after spinal cord injury

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Neural Regeneration Research 2018年第10期13卷 1787-1795页

作者： Mina P.Ghatas Robert M.Lester M.Rehan Khan Ashraf S.Gorgey Department of Spinal Cord Injury and Disorders Hunter Holmes McGuire VA Medical Center Department of Radiology Hunter Holmes McGuire VA Medical Center Department of Physical Medicine and Rehabilitation Virginia Commonwealth University

Magnetic resonance imaging is considered the ＂gold standard＂ technique for quantifying thigh muscle and fat cross-sectional area. We have developed a semi-automated technique to segment seven thigh compartments in persons with spinal cord injury. Thigh magnetic resonance images from 18 men（18–50 years old） with traumatic motor-complete spinal cord injury were analyzed in a blinded fashion using the threshold technique. The cross-sectional area values acquired by thresholding were compared to the manual tracing technique. The percentage errors for thigh circumference were（threshold： 170.71 ± 38.67; manual： 169.45 ± 38.27 cm2） 0.74%, subcutaneous adipose tissue（threshold： 65.99±30.79; manual： 62.68 ± 30.22） 5.2%, whole muscle（threshold： 98.18 ± 20.19; manual： 98.20 ± 20.08 cm2） 0.13%, femoral bone（threshold： 6.53 ± 1.09; manual： 6.53 ± 1.09 cm2） 0.64%, bone marrow fat（threshold： 3.12 ± 1.12; manual： 3.1 ± 1.11 cm2） 0.36%, knee extensor（threshold： 43.98 ± 7.66; manual： 44.61 ± 7.81 cm2） 1.78% and % intramuscular fat（threshold： 10.45 ± 4.29; manual： 10.92 ± 8.35%） 0.47%. Collectively, these results suggest that the threshold technique provided a robust accuracy in measuring the seven main thigh compartments, while greatly reducing the analysis time.

关键词： spinal cord injury magnetic resonance imaging semi-automated segmentation subcutaneous adipose tissue intramuscular fat

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semi-automated and interactive segmentation of contrast-enhancing masses on breast DCE-MRI using spatial fuzzy clustering

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2022年第PartA期71卷 103113-103113页

作者： Militello, Carmelo Rundo, Leonardo Dimarco, Mariangela Orlando, Alessia Conti, Vincenzo Woitek, Ramona D'Angelo, Ildebrando Bartolotta, Tommaso Vincenzo Russo, Giorgio Italian Natl Res Council IBFM CNR Inst Mol Bioimaging & Physiol I-90015 Cefalu PA Italy Univ Cambridge Dept Radiol Cambridge CB2 0QQ England Canc Res UK Cambridge Ctr Cambridge CB2 0RE England Univ Hosp Paolo Giaccone Dept Biomed Neurosci & Adv Diagnost BiND Sect Radiol I-90127 Palermo Italy Univ Enna KORE Fac Engn & Architecture I-94100 Enna Italy Med Univ Vienna Dept Biomed Imaging & Image Guided Therapy A-1090 Vienna Austria Fdn Ist G Giglio Dept Radiol I-90015 Cefalu PA Italy

Multiparametric Magnetic Resonance Imaging (MRI) is the most sensitive imaging modality for breast cancer detection and is increasingly playing a key role in lesion characterization. In this context, accurate and reliable quantification of the shape and extent of breast cancer is crucial in clinical research environments. Since conventional lesion delineation procedures are still mostly manual, automated segmentation approaches can improve this time-consuming and operator-dependent task by annotating the regions of interest in a reproducible manner. In this work, a semi-automated and interactive approach based on the spatial Fuzzy C-Means (sFCM) algorithm is proposed, used to segment masses on dynamic contrast-enhanced (DCE) MRI of the breast. Our method was compared against existing approaches based on classic image processing, namely (i) Otsu's method for thresholding-based segmentation, and (ii) the traditional FCM algorithm. A further comparison was performed against state-of-the-art Convolutional Neural Networks (CNNs) for medical image segmentation, namely SegNet and U-Net, in a 5-fold cross-validation scheme. The results showed the validity of the proposed approach, by significantly outperforming the competing methods in terms of the Dice similarity coefficient (84.47 +/- 4.75). Moreover, a Pearson's coefficient of rho = 0.993 showed a high correlation between segmented volume and the gold standard provided by clinicians. Overall, the proposed method was confirmed to outperform the competing literature methods. The proposed computer-assisted approach could be deployed into clinical research environments by providing a reliable tool for volumetric and radiomics analyses.

关键词： semi-automated segmentation Breast cancer Unsupervised fuzzy clustering Spatial information Computer-assisted lesion detection Magnetic Resonance Imaging

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semi-automated meningioma segmentation with bounding boxes

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Procedia Computer Science 2024年 245卷 583-590页

作者： Nur Adhianti Heryanto Mahmud Isnan Matthew Martianus Henry Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

segmentation is manually performed by physicians, which takes considerable time and may be subject to observers. Automating this task can increase efficiency and consistency. Existing studies on meningioma segmentation used data from limited study centers, indicating the need for research on multi-center data to assess generalizability. In this work, two semi-automated methods with bounding box priors, LiteMedSAM and BBU-Net, are evaluated on the brain tumor segmentation (BraTS) 2023 meningioma dataset collected from five study-centers. Preprocessing included exclusion of small tumors, z-score normalization, and extraction of slices that contain tumors, generating 25,602 2D axial magnetic resonance imaging (MRI) scans. A fine-tuning strategy is adopted for LiteMedSAM while BBU-Net is trained from scratch. The models are evaluated using a five-fold cross-validation, with data split at the case level. Results show that while U-Net models can achieve performance close to LiteMedSAM, the foundation model has overall better performance, with more than 90% in all evaluation scores.

关键词： Bounding boxes Meningioma semi-automated segmentation

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3D analysis of Osteosyntheses material using semi-automated CT segmentation: a case series of a 4 corner fusion plate

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BMC MUSCULOSKELETAL DISORDERS 2018年第20180226期19卷 52-52页

作者： Woehl, Rebecca Maier, Johannes Gehmert, Sebastian Palm, Christoph Riebschlaeger, Birgit Nerlich, Michael Huber, Michaela Univ Med Ctr Regensburg Dept Trauma Surg Franz Josef Str Allee 11 D-93053 Regensburg Germany Ostbayer Tech Hsch Fac Informat & Math Regensburg Germany Univ Clin Basel Dept Orthoped Surg Basel Switzerland Univ Med Ctr Regensburg Dept Neurol Regensburg Germany

Backround: Scaphoidectomy and midcarpal fusion can be performed using traditional fixation methods like K-wires, staples, screws or different dorsal (non) locking arthrodesis systems. The aim of this study is to test the Aptus four corner locking plate and to compare the clinical findings to the data revealed by CT scans and semi-automated segmentation. Methods: This is a retrospective review of eleven patients suffering from scapholunate advanced collapse (SLAC) or scaphoid non-union advanced collapse (SNAC) wrist, who received a four corner fusion between August 2011 and July 2014. The clinical evaluation consisted of measuring the range of motion (ROM), strength and pain on a visual analogue scale (VAS). Additionally, the Disabilities of the Arm, Shoulder and Hand (QuickDASH) and the Mayo Wrist Score were assessed. A computerized tomography (CT) of the wrist was obtained six weeks postoperatively. After semi-automated segmentation of the CT scans, the models were post processed and surveyed. Results: During the six-month follow-up mean range of motion (ROM) of the operated wrist was 60 degrees, consisting of 30 degrees extension and 30 degrees flexion. While pain levels decreased significantly, 54% of grip strength and 89% of pinch strength were preserved compared to the contralateral healthy wrist. Union could be detected in all CT scans of the wrist. While X-ray pictures obtained postoperatively revealed no pathology, two user related technical complications were found through the 3D analysis, which correlated to the clinical outcome. Conclusion: Due to semi-automated segmentation and 3D analysis it has been proved that the plate design can keep up to the manufacturers' promises. Over all, this case series confirmed that the plate can compete with the coexisting techniques concerning clinical outcome, union and complication rate.

关键词： 4CF SLAC wrist SNAC wrist semi-automated segmentation 3D analysis

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PRF-RW: a progressive random forest-based random walk approach for interactive semi-automated pulmonary lobes segmentation

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2020年第10期11卷 2221-2235页

作者： Li, Qiang Chen, Lei Li, Xiangju Lv, Xiaofeng Xia, Shuyue Kang, Yan Northeastern Univ Shenyang Liaoning Peoples R China Neusoft Med Syst Ltd Shenyang Liaoning Peoples R China Neusoft Corp Shenyang Liaoning Peoples R China Shenyang Med Coll Cent Hosp Shenyang Liaoning Peoples R China Shenzhen Technol Univ Shenzhen Peoples R China

The computational detection of lung lobes from computed tomography images is a challenging segmentation problem with important respiratory healthcare applications, including emphysema, chronic bronchitis, and asthma. This paper proposes a progressive random forest-based random walk approach for interactive semi-automated pulmonary lobes segmentation. First, our model performs automated segmentation of the lung lobes in a progressive random forest network, eliminating the need for prior segmentation of lungs, vessels, or airways. Then, an interactive lobes segmentation approach based on random walk mechanism is designed for improving auto-segmentation accuracy. Furthermore, we annotate a new dataset which contains 93 scans (57 men, 36 women;age range: 40-90 years) from the Central Hospital Affiliated with Shenyang Medical College (CHASMC). We evaluate the model on our annotated dataset, LIDC (https://*** ***) and LOLA11 (http://***/) datasets. The proposed model achieved a Dice score of 0.906 +/- 0.106 for LIDC, 0.898 +/- 0.113 for LOLA11, and 0.921 +/- 0.101 for our dataset. Experimental results show the accuracy of the proposed approach, which consistently improves performance across different datasets by a maximum of 8.2% as compared to baselines model.

关键词： Random forest Lobes segmentation Random walk semi-automated segmentation Machine learning

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semi-automated brain tumor and edema segmentation using MRI

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EUROPEAN JOURNAL OF RADIOLOGY 2005年第1期56卷 12-19页

作者： Xie, K Yang, J Zhang, ZG Zhu, YM Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai 200030 Peoples R China CNRS Res Unit 5515 CREATIS F-69621 Villeurbanne France INSERM U630 F-69621 Villeurbanne France

Purpose: Manual segmentation of brain tumors from magnetic resonance images is a challenging and time-consuming task. A semi-automated method has been developed for brain tumor and edema segmentation that will provide objective, reproducible segmentations that are close to the manual results. Additionally, the method segments non-enhancing brain tumor and edema from healthy tissues in magnetic resonance images. Methods and materials: In this study, a semi-automated method was developed for brain tumor and edema segmentation and volume measurement using magnetic resonance imaging (MRI). Some novel algorithms for tumor segmentation from MRI were integrated in this medical diagnosis system. We exploit a hybrid level set (HLS) segmentation method driven by region and boundary information simultaneously, region information serves as a propagation force which is robust and boundary information serves as a stopping functional which is accurate. Ten different patients with brain tumors of different size, shape and location were selected, a total of 246 axial tumor-containing slices obtained from 10 patients were used to evaluate the effectiveness of segmentation methods. Results: This method was applied to 10 non-enhancing brain tumors and satisfactory results were achieved. Two quantitative measures for tumor segmentation quality estimation, namely, correspondence ratio (CR) and percent matching (PM), were performed. For the segmentation of brain tumor, the volume total PM varies from 79.12 to 93.25% with the mean of 85.67 +/- 4.38% while the volume total CR varies from 0.74 to 0.91 with the mean of 0.84 +/- 0.07. For the segmentation of edema, the volume total PM varies from 72.86 to 87.29% with the mean of 79.54 +/- 4.18% while the volume total CR varies from 0.69 to 0.85 with the mean of 0.79 +/- 0.08. The HLS segmentation method perform better than the classical level sets (LS) segmentation method in PM and CR. Conclusions: The results of this research may have potenti

关键词： MRI semi-automated segmentation level set edema brain tumors

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A semi-automated volumetric software for segmentation and perfusion parameter quantification of brain tumors using 320-row multidetector computed tomography: a validation study

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NEURORADIOLOGY 2017年第5期59卷 461-469页

作者： Chae, Soo Young Suh, Sangil Ryoo, Inseon Park, Arim Noh, Kyoung Jin Shim, Hackjoon Seol, Hae Young Korea Univ Dept Radiol Guro Hosp 148 Gurodong Ro Seoul South Korea Soonchunhyang Univ Dept Elect Engn Asan South Korea Toshiba Med Syst Korea Co Seoul South Korea

Purpose We developed a semi-automated volumetric software, NPerfusion, to segment brain tumors and quantify perfusion parameters on whole-brain CT perfusion (WBCTP) images. The purpose of this study was to assess the feasibility of the software and to validate its performance compared with manual segmentation. Methods Twenty-nine patients with pathologically proven brain tumors who underwent preoperative WBCTP between August 2012 and February 2015 were included. Three perfusion parameters, arterial flow (AF), equivalent blood volume (EBV), and Patlak flow (PF, which is a measure of permeability of capillaries), of brain tumors were generated by a commercial software and then quantified volumetrically by NPerfusion, which also semi-automatically segmented tumor boundaries. The quantification was validated by comparison with that of manual segmentation in terms of the concordance correlation coefficient and Bland-Altman analysis. Resuts With NPerfusion, we successfully performed segmentation and quantified whole volumetric perfusion parameters of all 29 brain tumors that showed consistent perfusion trends with previous studies. The validation of the perfusion parameter quantification exhibited almost perfect agreement with manual segmentation, with Lin concordance correlation coefficients (rho(c)) for AF, EBV, and PF of 0.9988, 0.9994, and 0.9976, respectively. On Bland-Altman analysis, most differences between this software and manual segmentation on the commercial software were within the limit of agreement. Conclusions NPerfusion successfully performs segmentation of brain tumors and calculates perfusion parameters of brain tumors. We validated this semi-automated segmentation software by comparing it with manual segmentation. NPerfusion can be used to calculate volumetric perfusion parameters of brain tumors from WBCTP.

关键词： Brain tumor CT perfusion Whole-brain CT perfusion segmentation semi-automated segmentation

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semi-automated 3D segmentation of human skeletal muscle using Focused Ion Beam-Scanning Electron Microscopic images

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JOURNAL OF STRUCTURAL BIOLOGY 2019年第1期207卷 1-11页

作者： Caffrey, Brian J. Maltsev, Alexander, V Gonzalez-Freire, Marta Hartnell, Lisa M. Ferrucci, Luigi Subramaniam, Sriram Univ British Columbia Vancouver BC Canada NIA Longitudinal Studies Sect NIH Baltimore MD 21225 USA NCI Lab Cell Biol Ctr Canc Res NIH Bethesda MD 20892 USA

Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) is an imaging approach that enables analysis of the 3D architecture of cells and tissues at resolutions that are 1-2 orders of magnitude higher than that possible with light microscopy. The slow speeds of data collection and manual segmentation are two critical problems that limit the more extensive use of FIB-SEM technology. Here, we present an easily accessible robust method that enables rapid, large-scale acquisition of data from tissue specimens, combined with an approach for semi-automated data segmentation using the open-source machine learning Weka segmentation software, which dramatically increases the speed of image analysis. We demonstrate the feasibility of these methods through the 3D analysis of human muscle tissue by showing that our process results in an improvement in speed of up to three orders of magnitude as compared to manual approaches for data segmentation. All programs and scripts we use are open source and are immediately available for use by others.

关键词： FIB-SEM 3D electron microscopy Machine learning Aging Skeletal muscle Mitochondrial structure semi-automated segmentation Tissue imaging

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Clinical feasibility of a myocardial signal intensity threshold-based semi-automated cardiac magnetic resonance segmentation method

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EUROPEAN RADIOLOGY 2016年第5期26卷 1503-1511页

作者： Varga-Szemes, Akos Muscogiuri, Giuseppe Schoepf, U. Joseph Wichmann, Julian L. Suranyi, Pal De Cecco, Carlo N. Cannao, Paola M. Renker, Matthias Mangold, Stefanie Fox, Mary A. Ruzsics, Balazs Med Univ S Carolina Dept Radiol & Radiol Sci Div Cardiovasc Imaging Ashley River Tower25 Courtenay Dr Charleston SC 29425 USA Univ Roma La Sapienza Dept Med Surg Sci & Translat Med Piazzale Aldo Moro 5 I-00185 Rome Italy Univ Hosp Frankfurt Dept Diagnost & Intervent Radiol Frankfurt Germany Univ Milan Scuola Specializzaz Radiodiagnost Milan Italy Kerckhoff Heart & Thorax Ctr Bad Nauheim Germany Univ Tubingen Dept Diagnost & Intervent Radiol Tubingen Germany Royal Liverpool & Broadgreen Univ Hosp Dept Cardiol Liverpool Merseyside England

To assess the accuracy and efficiency of a threshold-based, semi-automated cardiac MRI segmentation algorithm in comparison with conventional contour-based segmentation and aortic flow measurements. Short-axis cine images of 148 patients (55 +/- 18 years, 81 men) were used to evaluate left ventricular (LV) volumes and mass (LVM) using conventional and threshold-based segmentations. Phase-contrast images were used to independently measure stroke volume (SV). LV parameters were evaluated by two independent readers. Evaluation times using the conventional and threshold-based methods were 8.4 +/- 1.9 and 4.2 +/- 1.3 min, respectively (P < 0.0001). LV parameters measured by the conventional and threshold-based methods, respectively, were end-diastolic volume (EDV) 146 +/- 59 and 134 +/- 53 ml;end-systolic volume (ESV) 64 +/- 47 and 59 +/- 46 ml;SV 82 +/- 29 and 74 +/- 28 ml (flow-based 74 +/- 30 ml);ejection fraction (EF) 59 +/- 16 and 58 +/- 17 %;and LVM 141 +/- 55 and 159 +/- 58 g. Significant differences between the conventional and threshold-based methods were observed in EDV, ESV, and LVM mesurements;SV from threshold-based and flow-based measurements were in agreement (P > 0.05) but were significantly different from conventional analysis (P < 0.05). Excellent inter-observer agreement was observed. Threshold-based LV segmentation provides improved accuracy and faster assessment compared to conventional contour-based methods. aEuro cent Threshold-based left ventricular segmentation provides time-efficient assessment of left ventricular parameters aEuro cent The threshold-based method can discriminate between blood and papillary muscles aEuro cent This method provides improved accuracy compared to aortic flow measurements as a reference.

关键词： Left ventricular function Left ventricular mass Aortic flow Cine magnetic resonance imaging semi-automated segmentation

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Development and validation of a semi-automated and unsupervised method for femur segmentation from CT

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SCIENTIFIC REPORTS 2024年第1期14卷 1-13页

作者： Aldieri, Alessandra Biondi, Riccardo La Mattina, Antonino A. Szyszko, Julia A. Polizzi, Stefano Dall'Olio, Daniele Curti, Nico Castellani, Gastone Viceconti, Marco Politecn Torino Dept Mech & Aerosp Engn PolitoBIOMedLab Turin Italy IRCCS Ist Ortoped Rizzoli Med Technol Lab Bologna Italy IRCCS Bologna Ist Sci Neurol Bologna Bologna Italy Alma Mater Studiorum Univ Bologna Dept Med & Surg Sci Bologna Italy Alma Mater Studiorum Univ Bologna Dept Ind Engn Bologna Italy Alma Mater Studiorum Univ Bologna Dept Phys & Astron Bologna Italy

Quantitative computed tomography (QCT)-based in silico models have demonstrated improved accuracy in predicting hip fractures with respect to the current gold standard, the areal bone mineral density. These models require that the femur bone is segmented as a first step. This task can be challenging, and in fact, it is often almost fully manual, which is time-consuming, operator-dependent, and hard to reproduce. This work proposes a semi-automated procedure for femur bone segmentation from CT images. The proposed procedure is based on the bone and joint enhancement filter and graph-cut algorithms. The semi-automated procedure performances were assessed on 10 subjects through comparison with the standard manual segmentation. Metrics based on the femur geometries and the risk of fracture assessed in silico resulting from the two segmentation procedures were considered. The average Hausdorff distance (0.03 +/- 0.01 mm) and the difference union ratio (0.06 +/- 0.02) metrics computed between the manual and semi-automated segmentations were significantly higher than those computed within the manual segmentations (0.01 +/- 0.01 mm and 0.03 +/- 0.02). Besides, a blind qualitative evaluation revealed that the semi-automated procedure was significantly superior (p < 0.001) to the manual one in terms of fidelity to the CT. As for the hip fracture risk assessed in silico starting from both segmentations, no significant difference emerged between the two (R-2 = 0.99). The proposed semi-automated segmentation procedure overcomes the manual one, shortening the segmentation time and providing a better segmentation. The method could be employed within CT-based in silico methodologies and to segment large volumes of images to train and test fully automated and supervised segmentation methods.

关键词： Bone and joint enhancement filter CT Femur segmentation Finite element model Graph-cut semi-automated segmentation

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