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Automatic liver and tumor segmentation based on deep learning and globally optimized refinement

Applied Mathematics(A Journal of Chinese Universities) 2021年第2期36卷 304-316页

作者： HONG Yuan MAO Xiong-wei HUI Qing-lei OUYANG Xiao-ping PENG Zhi-yi KONG De-xing School of Mathematical Science Zhejiang UniversityHangzhou 310058China Department of Radiology Zhejiang University HospitalHangzhou 310058China Department of Radiology The First Afiliated HospitalZhejiang University School of MedicineHangzhou 310003China School of Mechanical Engineering Zhejiang UniversityHangzhou 310058China

Automatic segmentation of the liver and hepatic lesions from abdominal 3D comput-ed tomography(CT)images is fundamental tasks in computer-assisted liver surgery ***,due to complex backgrounds,ambiguous boundaries,heterogeneous appearances and highly varied shapes of the liver,accurate liver segmentation and tumor detection are stil-1 challenging *** address these difficulties,we propose an automatic segmentation framework based on 3D U-net with dense connections and globally optimized ***-ly,a deep U-net architecture with dense connections is trained to learn the probability map of the *** the probability map goes into the following refinement step as the initial surface and prior *** segmentation of liver tumor is based on the similar network architecture with the help of segmentation results of *** order to reduce the infuence of the surrounding tissues with the similar intensity and texture behavior with the tumor region,during the training procedure,I x liverlabel is the input of the network for the segmentation of liver *** do-ing this,the accuracy of segmentation can be *** proposed method is fully automatic without any user *** qualitative and quantitative results reveal that the pro-posed approach is efficient and accurate for liver volume estimation in clinical *** high correlation between the automatic and manual references shows that the proposed method can be good enough to replace the time-consuming and non-reproducible manual segmentation method.

关键词： liver segmentation tumor segmentation CT deep learning

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WNet:An efficient network for 3D brain tumor segmentation

WNet:An efficient network for 3D brain tumor segmentation

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第十七届中国体视学与图像分析学术会议

作者：李崇夏勇 School of Computer Science and Engineering Northwestern Polytechnical University

Automated segmentation of brain tumors using multi-modal MR imaging is essential in assisting medical professionals to evaluate tumor development and make fast therapeutic *** the progress achieved by U-like network(UNet) and its variants on 2 D medical image segmentation,3 D brain tumor segmentation still suffers from high computation complexity due to the large size of 3 D *** this paper,we propose a W-like network(WNet) for effective and efficient 3 D brain tumor *** reduce the high computation complexity of UNet,we attempt to "decompose",respectively,each MRI volume into a low-resolution data that has a low spatial resolution and a thick-slice data that has less ***,we extend the one-path encoder of UNet to a two-path encoder,i.e.a low-resolution pathway and thick-slice pathway,which take the decomposed data as input and focus on capturing the interand intra-slice semantics with less computational *** on a shared decoder,we recover the segmentation details by fusing and up-sampling the features from both *** evaluated WNet on the Brain tumor segmentation 2018 Challenge *** results suggest that decomposing data leads to lower computation complexity but better performance than UNet,and our proposed WNet is able to achieve competitive performance in the segmentation of enhancing tumor,whole tumor,and tumor core during the online testing.

关键词： tumor segmentation data decomposition

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Accuracy of Three-dimesional segmentation of Benign Parotid tumor Using THUMedical Software

Accuracy of Three-dimesional Segmentation of Benign Parotid ...

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中华口腔医学会第14次全国口腔医学学术会议（2012年会）

作者： SMD candidate:YuHuan Chen Supervisor:Prof.Chuan-bin Guo Department of Oral and Maxillofacial Surgery Peking University of School of Stomatology

OBJECTIVES:THUMedical software is a precise,stable and efficient maxillofacial surgical navigation system software developed from 863 project"Precise Craniomaxillofacial Robotic Therapy System".Graph cut 3D segmentation algorithm is one of the innovation of the *** accuracy of tumor 3D segmentation using THUMedical software was to assess its clinical application value. Methods:Thirty patients with benign parotid tumor undergoing parotidectomyin the Department of Oral and Maxillofacial Surgery of Peking University of School of Stomatology entered the *** CT data of the candidates were obtained fromDepartment of radiology in our *** to CT data tumor 3D segmentations were performed by THUMedical software and Iplan CMF software *** of the softwares calculated the tumor *** true tumor physical volumes were measured by water displacement method after parotidectomy. tumor volumes measured by three approaches were compared postoperatively by SPSS 16.0 software. Results:There was a significant difference between Vt and Vw.(P = 0.008 0.05)In enhanced CT group(N = 10)Vt was 9.743±4.956 cm,Vw was 9.696±5.064cm. There was no significant difference between Vt and Vw(P = 0.829> 0.05).The average absolute error between Vt and Vw was 0.160±0.109cm～3(0.051 cm～0.269cm),the average relative error between Vt and Vwwas 2.10±1.29%(-0.81%～3.39%).There was a significant linear correlation between Vt and *** improved time-saving efficiency 6 to 7 times compared to Iplan CMF software. Conclusion:THUMedical software based on Graph cut algorithmisaccurate and semiautomaticin three-dimensional segmentation of benign parotid *** provides tumor volume effectively and predicts true tumor volume size *** time consuming is much lower than that of Iplan the CMF based on manual contouring. THUMedical software is valuable in clinical application.

关键词： Three-dimensional segmentation tumor volume tumor segmentation Graph cut algorithm Accuracy

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Brain tumor Detection and Classification Using PSO and Convolutional Neural Network

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Computers, Materials & Continua 2022年第12期73卷 4501-4518页

作者： Muhammad Ali Jamal Hussain Shah Muhammad Attique Khan Majed Alhaisoni Usman Tariq Tallha Akram Ye Jin Kim Byoungchol Chang Department of Computer Science COMSATS University IslamabadWah CampusPakistan Departmnt of Computer Science HITEC University TaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of EE COMSATS University IslamabadWah CampusPakistan Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

tumor detection has been an active research topic in recent years due to the high mortality *** vision(CV)and image processing techniques have recently become popular for detecting tumors inMRI *** automated detection process is simpler and takes less time than manual *** addition,the difference in the expanding shape of brain tumor tissues complicates and complicates tumor detection for *** proposed a newframework for tumor detection aswell as tumor classification into relevant categories in this *** tumor segmentation,the proposed framework employs the Particle Swarm Optimization(PSO)algorithm,and for classification,the convolutional neural network(CNN)*** preprocessing techniques such as noise removal,image sharpening,and skull stripping are used at the start of the segmentation ***,PSO-based segmentation is *** the classification step,two pre-trained CNN models,alexnet and inception-V3,are used and trained using transfer *** a serial approach,features are extracted from both trained models and fused features for final *** classification,a variety of machine learning classifiers are *** dice values on datasets BRATS-2018 and BRATS-2017 are 98.11 percent and 98.25 percent,respectively,whereas average jaccard values are 96.30 percent and 96.57%(segmentation Results).The results were extended on the same datasets for classification and achieved 99.0%accuracy,sensitivity of 0.99,specificity of 0.99,and precision of ***,the proposed method is compared to state-of-the-art existingmethods and outperforms them.

关键词： Magnetic resonance imaging(MRI) tumor segmentation deep learning features extraction classification

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Leveraging EfficientNetB3 in a Deep Learning Framework for High-Accuracy MRI tumor Classification

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Computers, Materials & Continua 2024年第10期81卷 867-883页

作者： Mahesh Thyluru Ramakrishna Kuppusamy Pothanaicker Padma Selvaraj Surbhi Bhatia Khan Vinoth Kumar Venkatesan Saeed Alzahrani Mohammad Alojail Department of Computer Science&Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bengaluru562112India School of Computer Science and Engineering VIT-AP UniversityAmaravati522241India Department of Computer Science&Technology Madanapalle Institute of Technology&ScienceMadanapalle517325India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK School of Computer Science Engineering&Information Systems(SCORE) Vellore Institute of Technology(VIT)Vellore632014India Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh11531Saudi Arabia Adjunct Research Faculty Centre for Research Impact&OutcomeChitkara UniversityRajpuraPunjab140401India

Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient *** different types of brain tumors,including gliomas,meningiomas,pituitary tumors,as well as confirming the absence of tumors,poses a significant challenge using MRI *** approaches predominantly rely on traditional machine learning and basic deep learning methods for image *** methods often rely on manual feature extraction and basic convolutional neural networks(CNNs).The limitations include inadequate accuracy,poor generalization of new data,and limited ability to manage the high variability in MRI *** the EfficientNetB3 architecture,this study presents a groundbreaking approach in the computational engineering domain,enhancing MRI-based brain tumor *** approach highlights a major advancement in employing sophisticated machine learning techniques within Computer Science and Engineering,showcasing a highly accurate framework with significant potential for healthcare *** model achieves an outstanding 99%accuracy,exhibiting balanced precision,recall,and F1-scores across all tumor types,as detailed in the classification *** successful implementation demonstrates the model’s potential as an essential tool for diagnosing and classifying brain tumors,marking a notable improvement over current *** integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency,paving the way for wider *** research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology.

关键词： Deep learning MRI brain tumor cassification EfficientNetB3 computational engineering healthcare technology artificial intelligence in medical imaging tumor segmentation neuro-oncology

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Automatic segmentation of Liver from Abdominal Computed Tomography Images Using Energy Feature

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Computers, Materials & Continua 2021年第4期67卷 709-722页

作者： Prabakaran Rajamanickam Shiloah Elizabeth Darmanayagam Sunil Retmin Raj Cyril Raj University Department of Computer Science Engineering Anna UniversityChennai600025India University Department of Information Technology MIT CampusChennai600044India

Liver segmentation is one of the challenging tasks in detecting and classifying liver tumors from Computed Tomography(CT)*** segmentation of hepatic organ is more intricate task,owing to the fact that it possesses a sizeable quantum of *** paper proposes an algorithm for automatic seed point selection using energy feature for use in level set algorithm for segmentation of liver region in CT *** effectiveness of the method can be determined when used in a model to classify the liver CT images as tumorous or *** involves segmentation of the region of interest(ROI)from the segmented liver,extraction of the shape and texture features from the segmented ROI and classification of the ROIs as tumorous or not by using a classifier based on the extracted *** this work,the proposed seed point selection technique has been used in level set algorithm for segmentation of liver region in CT scans and the ROIs have been extracted using Fuzzy C Means clustering(FCM)which is one of the algorithms to segment the *** dataset used in this method has been collected from various repositories and scan *** outcome of this proposed segmentation model has reduced the area overlap error that could offer the intended accuracy and *** gives better results when compared with other existing *** execution in short span of time is another advantage of this method which in turns helps the radiologist to ascertain the abnormalities instantly.

关键词： Liver segmentation automatic seed point tumor segmentation classification fuzzy C means clustering

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Efficient Computer Aided Diagnosis System for Hepatic tumors Using Computed Tomography Scans

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Computers, Materials & Continua 2022年第6期71卷 4871-4894页

作者： Yasmeen Al-Saeed Wael A.Gab-Allah Hassan Soliman Maysoon F.Abulkhair Wafaa M.Shalash Mohammed Elmogy Faculty of Computers and Information Mansoura UniversityMansoura35516Egypt Faculty of Computers&Artificial Intelligence South Valley UniversityHurghada84511Egypt Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityBanha13511Egypt

One of the leading causes of mortality worldwide is liver *** earlier the detection of hepatic tumors,the lower the mortality *** paper introduces a computer-aided diagnosis system to extract hepatic tumors from computed tomography scans and classify them into malignant or benign *** hepatic tumors from computed tomography scans is considered a challenging task due to the fuzziness in the liver pixel range,intensity values overlap between the liver and neighboring organs,high noise from computed tomography scanner,and large variance in tumors *** proposed method consists of three main stages;liver segmentation using Fast Generalized Fuzzy C-Means,tumor segmentation using dynamic thresholding,and the tumor’s classification into malignant/benign using support vector machines *** performance of the proposed system was evaluated using three liver benchmark datasets,which are MICCAI-Sliver07,LiTS17,and *** proposed computer adided diagnosis system achieved an average accuracy of 96.75%,sensetivity of 96.38%,specificity of 95.20%and Dice similarity coefficient of 95.13%.

关键词： Liver tumor hepatic tumors diagnosis CT scans analysis liver segmentation tumor segmentation features extraction tumors classification FGFCM CAD system

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Short‐term and long‐term memory self‐attention network for segmentation of tumours in 3D medical images

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1524-1537页

作者： Mingwei Wen Quan Zhou Bo Tao Pavel Shcherbakov Yang Xu Xuming Zhang Department of Biomedical Engineering College of Life Science and TechnologyHuazhong University of Science and TechnologyWuhanChina State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhanChina Institute for Control Science Russian Academy of SciencesMoscowRussia Hubei Medical Devices Quality Supervision and Test Institute WuhanChina

Tumour segmentation in medical images(especially 3D tumour segmentation)is highly challenging due to the possible similarity between tumours and adjacent tissues,occurrence of multiple tumours and variable tumour shapes and *** popular deep learning‐based segmentation algorithms generally rely on the convolutional neural network(CNN)and *** former cannot extract the global image features effectively while the latter lacks the inductive bias and involves the complicated computation for 3D volume *** existing hybrid CNN‐Transformer network can only provide the limited performance improvement or even poorer segmentation performance than the pure *** address these issues,a short‐term and long‐term memory self‐attention network is ***,a distinctive self‐attention block uses the Transformer to explore the correlation among the region features at different levels extracted by the ***,the memory structure filters and combines the above information to exclude the similar regions and detect the multiple ***,the multi‐layer reconstruction blocks will predict the tumour *** results demonstrate that our method outperforms other methods in terms of subjective visual and quantitative *** with the most competitive method,the proposed method provides Dice(82.4%vs.76.6%)and Hausdorff distance 95%(HD95)(10.66 vs.11.54 mm)on the KiTS19 as well as Dice(80.2%vs.78.4%)and HD95(9.632 vs.12.17 mm)on the LiTS.

关键词： 3D medical images convolutional neural network self‐attention network transformer tumor segmentation

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segmentation of Head and Neck tumors Using Dual PET/CT Imaging:Comparative Analysis of 2D,2.5D,and 3D Approaches Using UNet Transformer

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Computer Modeling in Engineering & Sciences 2024年第12期141卷 2351-2373页

作者： Mohammed A.Mahdi Shahanawaj Ahamad Sawsan A.Saad Alaa Dafhalla Alawi Alqushaibi Rizwan Qureshi Information and Computer Science Department College of Computer Science and EngineeringUniversity of Ha’ilHa’il55476Saudi Arabia Software Engineering Department College of Computer Science and EngineeringUniversity of Ha’ilHa’il55476Saudi Arabia Computer Engineering Department College of Computer Science and EngineeringUniversity of Ha’ilHa’il55476Saudi Arabia Department of Computer and Information Sciences Universiti Teknologi PetronasSeri Iskandar32610Malaysia Center for Research in Computer Vision(CRCV) University of Central FloridaOrlandoFL 32816USA

The segmentation of head and neck(H&N)tumors in dual Positron Emission Tomography/Computed Tomogra-phy(PET/CT)imaging is a critical task in medical imaging,providing essential information for diagnosis,treatment planning,and outcome *** by the need for more accurate and robust segmentation methods,this study addresses key research gaps in the application of deep learning techniques to multimodal medical ***,it investigates the limitations of existing 2D and 3D models in capturing complex tumor structures and proposes an innovative 2.5D UNet Transformer model as a *** primary research questions guiding this study are:(1)How can the integration of convolutional neural networks(CNNs)and transformer networks enhance segmentation accuracy in dual PET/CT imaging?(2)What are the comparative advantages of 2D,2.5D,and 3D model configurations in this context?To answer these questions,we aimed to develop and evaluate advanced deep-learning models that leverage the strengths of both CNNs and *** proposed methodology involved a comprehensive preprocessing pipeline,including normalization,contrast enhancement,and resampling,followed by segmentation using 2D,2.5D,and 3D UNet Transformer *** models were trained and tested on three diverse datasets:HeckTor2022,AutoPET2023,and *** was assessed using metrics such as Dice Similarity Coefficient,Jaccard Index,Average Surface Distance(ASD),and Relative Absolute Volume Difference(RAVD).The findings demonstrate that the 2.5D UNet Transformer model consistently outperformed the 2D and 3D models across most metrics,achieving the highest Dice and Jaccard values,indicating superior segmentation *** instance,on the HeckTor2022 dataset,the 2.5D model achieved a Dice score of 81.777 and a Jaccard index of 0.705,surpassing other model *** 3D model showed strong boundary delineation performance but exhibited variability across datasets,while the

关键词： PET/CT imaging tumor segmentation weighted fusion transformer multi-modal imaging deep learning neural networks clinical oncology

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Artificial intelligence-based medical image segmentation for 3D printing and naked eye 3D visualization

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Intelligent Medicine 2022年第1期2卷 48-53页

作者： Guang Jia Xunan Huang Sen Tao Xianghuai Zhang Yue Zhao Hongcai Wang Jie He Jiaxue Hao Bo Liu Jiejing Zhou Tanping Li Xiaoling Zhang Jinglong Gao School of Computer Science and Technology Xidian UniversityXi’anShaanxi 710068China Shaanxi Xinweitai Biological Technology Co. Ltd.Xi’anShaanxi 710065China Department of Radiation Oncology Tangdu Hospital Affiliated to Air Force Medical UniversityXi’anShaanxi 710071China School of Physics and Optoelectronic Engineering Xidian UniversityXi’anShaanxi 710071China Shaanxi Provincial People’s Hospital Xi’anShaanxi 710068China

Image segmentation for 3D printing and 3D visualization has become an essential component in many fields of medical research,teaching,and clinical *** image segmentation requires sophisticated computerized quantifications and visualization ***,with the development of artificial intelligence(AI)technology,tumors or organs can be quickly and accurately detected and automatically contoured from medical *** paper introduces a platform-independent,multi-modality image registration,segmentation,and 3D visualization program,named artificial intelligence-based medical image segmentation for 3D printing and naked eye 3D visualization(AIMIS3D).YOLOV3 algorithm was used to recognize prostate organ from T2-weighted MRI images with proper *** cancer and bladder cancer were segmented based on U-net from MRI *** images of osteosarcoma were loaded into the platform for the segmentation of lumbar spine,osteosarcoma,vessels,and local nerves for 3D *** displacement during each radiation therapy was quantitatively evaluated by automatically identifying the position of the 3D printed plastic breast *** vessel from multimodality MRI images was segmented by using model-based transfer learning for 3D printing and naked eye 3D visualization in AIMIS3D platform.

关键词： Medical image segmentation Artificial intelligence tumor segmentation 3D printing Voice recognition Gesture recognition

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