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2025 International Symposium on Innovative Informatics of Biskra, ISNIB 2025

作者： Abdellaoui, Amal Benameur, Narjes Zarrouk, Rim Mahmoudi, Ramzi Bedoui, Mohamed Hedi Faculty of Medicine of Monastir Medical Imaging Technology Lab - LTIMLR12ES06 University of Monastir Tunisia Higher Institute of Medical Technologies of Tunis Laboratory of Biophysics and Medical Technologies University of Tunis El Manar Tunisia University of Gafsa Faculty of Sciences Tunisia University of Monastir Faculty of Medicine Laboratory of Technology and Medical Imaging Monastir 5000 Tunisia Gustave Eiffel University Gaspard-Monge Computer Science Laboratory Esiee Paris Cité Descartes Noisy Le Grand 93162 France

ISBN: (纸本)9798331509576

The liver's complexity in structure and function makes it susceptible to various forms of damage, highlighting the need for prompt and accurate diagnosis of liver tumors to ensure timely medical intervention. Image processing has emerged as a valuable tool for diagnostic support, especially in complex liver surgeries. This study presents a deep learning-based approach for automated liver segmentation on magnetic resonance imaging (MRI) using a u-net model. Our approach is designed to enhance preoperative planning by improving image analysis and aiding surgical teams in reducing potential complications. We utilized a public MRI dataset of 310 liver image series, representing diverse clinical cases and contrast types. data augmentation techniques were applied to increase the model's robustness. After selecting the most suitable contrast type, we trained the u-net model and evaluated its performance using metrics focused on segmentation accuracy. The model achieved a dice Similarity Coefficient (dSC) of 94.37% and a 95th Percentile Hausdorff distance (Hd95) of 3.99 mm, demonstrating high precision in liver segmentations. These results emphasize the potential of this model as a reliable, MRI-based tool for clinical support in liver surgery planning and risk management. © 2025 IEEE.

关键词： 2d u-net Axial Opposed deep Learning Liver MRI Images segmentation

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Evaluation of Trident network against atlas and 2d u-net methods for auto-segmentation of organs at risk in nasopharyngeal carcinoma: a comparative study

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FRONTIERS IN PHYSICS 2025年 13卷

作者： Pan, Jinghui Zhuo, Fengjiao Li, dan Li, Xiangpan Song, Qibin Jiang, Hao Xiao, Jinsheng Ruan, Changli Wuhan Univ Sch Elect Informat Wuhan Hubei Peoples R China Wuhan Univ Renmin Hosp Dept Radiat Oncol Wuhan Hubei Peoples R China Jiangling Cty Peoples Hosp Dept Radiat Oncol Jingzhou Hubei Peoples R China Huazhong Univ Sci & Technol Hubei Canc Hosp Tongji Med Coll Dept Radiat Oncol Wuhan Hubei Peoples R China

Background and objective(s) This study evaluates the accuracy of automatic segmentation of Organs at Risk (OARs) in nasopharyngeal carcinoma (NPC) using three approaches: atlas-based, 2d u-net, and a self-developed Trident network. Our aim was to develop, validate, and compare the performance of Trident network for precise delineation of NPC using our center's dataset, in comparison to conventional atlas-based and 2d u-net *** and methods We randomly selected 209 patients with NPC for this retrospective study, with OARs manually delineated by physicians. An atlas template library was generated using data from seventeen OARs obtained from a subset of these patients, while the remaining 29 cases constituted the test set. The performance of auto-delineation methods, including Atlas, 2d u-net, and the Trident network, was compared to manual delineations. Accuracy was evaluated using the dice Similarity Coefficient (dSC) and Hausdorff distance (Hd), alongside timing each method. Statistical analysis included one-way ANOVA for normally distributed data, Welch's test for data with uneven variance, and Post Hoc Comparison tests [Least Significant difference (LSd) and Tamhane's T2] for pairwise comparisons. The Kruskal-Wallis H test was employed for non-normally distributed *** In the evaluation of segmentation results for all 23 OARs in NPC, the Trident network achieved the highest dSC (0.87 +/- 0.07), significantly outperforming the Atlas (0.67 +/- 0.02) and 2d u-net (0.71 +/- 0.02). Additionally, the mean Hd values for all the three methods were below 5 mm. In particular, with the exception of the right eyeball, the Trident network demonstrated superior dSC for each organ compared to the other two methods. The Trident network showed high morphological similarity from Atlas and 2d u-net in most structures, performing significantly better than Atlas for multiple structures such as the Hypophysis, Optic chiasma, Esophagus, and others. Furthermore, i

关键词： nasopharyngeal carcinoma atlas trident network 2d u-net organs at risk

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Brain Tumor Segmentation using dense Channels 2d u-net and Multiple Feature Extraction network 5th

Brain Tumor Segmentation Using Dense Channels 2D U-net and M...

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5th International MICCAI Brain-Lesion Workshop (BrainLes)

作者： Shi, Wei Pang, Enshuai Wu, Qiang Lin, Fengming Shandong Univ Sch Informat Sci & Engn Jinan Peoples R China Shandong Univ Inst Brain & Brain Inspired Sci Jinan Peoples R China

ISBN: (纸本)9783030466398;9783030466404

Semantic segmentation plays an important role in the prevention, diagnosis and treatment of brain glioma. In this paper, we propose a dense channels 2d u-net segmentation model with residual unit and feature pyramid unit. The main difference compared with other u-net models is that the number of bottom feature components is increased, so that the network can learn more abundant patterns. We also develop a multiple feature extraction network model to extract rich and diverse features, which is conducive to segmentation. Finally, we employ decision tree regression model to predict patient overall survival by the different texture, shape and first-order features extracted from BraTS 2019 dataset.

关键词： Brain tumor segmentation 2d u-net dense channels Multiple feature extraction Feature pyramid

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MRI image analysis for cerebrum tumor detection and feature extraction using 2d u-Convnet and SVM classification

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PERSONAL ANd uBIQuITOuS COMPuTING 2022年第3期27卷 931-940页

作者： Pitchai, R. Supraja, P. Sulthana, A. Razia Veeramakali, T. Babu, Ch Madhu BV Raju Inst Technol Dept Comp Sci & Engn Narsapur Telangana India SRM Inst Sci & Technol Sch Comp Dept Networking & Commun Kattankulathur Tamil Nadu India BITS Pilani Dept Comp Sci Dubai U Arab Emirates SRM Inst Sci & Technol Sch Comp Dept Data Sci & Business Syst Kattankulathur Tamil Nadu India

Precise tumor segmentation plays a significant role in radio surgery arrangement and the evaluation of radiotherapy treatment efficiently. To increase the performance and lessen the unpredictability included in the medical image segmentation, transform-based 2d u-Convnet cerebrum tumor segmentation has been explored. Additionally, to improve the exactness of the support vector machine (SVM) classification, appropriate attributes are extricated from every segmented tissue. The simulation outcomes of this method have been assessed and confirmed for the performance and quality validation of MRI images based on various performance evaluation parameters. This method accomplished the segmentation accuracy of 93.8%, the sensitivity of 87.6%, the specificity of 94.8%, and the recall of 85.6%, exhibiting the efficacy of the proposed method for recognizing ordinary and tumor tissues from MRI images.

关键词： MRI images Segmentation 2d u-net SVM classification discrete wavelet transform

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Effects of Cropping vs Resizing on the Performance of Brain Tumor Segmentation Models

Effects of Cropping vs Resizing on the Performance of Brain ...

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12th IEEE International Conference on Computer, Information and Telecommunication Systems (CITS)

作者： Magboo, Ma Sheila A. Coronel, Andrei d. Univ Philippines Manila Coll Arts & Sci Dept Phys Sci & Math Manila Philippines Ateneo Manila Univ Sch Sci & Engn Dept Informat Syst & Comp Sci Katipunan Ave Quezon City Philippines

ISBN: (纸本)9798350359107;9798350359091

This study investigated the effects of image resizing vs cropping on the performance of state-of-the-art models for the brain tumor segmentation task. This is particularly important since many studies simply resize the image without thinking about potential effects of image distortion on the model's segmentation performance. Since the objective of tumor segmentation is to predict the pixels that comprise the actual tumor, image cropping was performed in order to focus more on the brain and the tumor and not on the background. This conjecture was tested using state-of-the-art models namely 2d u-net, 2d u- net with VGG19 as backbone, 2d u-net with InceptionV3 ad backbone, and 2d u-net with InceptionResnetV2 as backbone. Three different configurations were designed for this purpose. The first configuration used resized images while the second configuration used cropped images. The third configuration used pretrained weights of models of trained on the resized images and then applied them on the cropped images. Overall, the top three models are 2d u-net with InceptionResnetV2 as backbone trained using the resized images followed by 2d u-net trained also using the resized images and then finally by 2d u-net trained using the cropped images. As to why cropping did not perform well in this experiment, several plausible explanations were provided in this study.

关键词： 2d u-net 2d u-net InceptionResnetV2 image resizing image cropping brain tumor segmentation

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Phase arrival picking for bridging multi-source downhole microseismic data using deep transfer learning

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JOuRNAL OF GEOPHYSICS ANd ENGINEERING 2022年第2期19卷 178-191页

作者： Zhang, Yilun Leng, Jiaxuan dong, Yihan Yu, Zhichao Hu, Tianyue He, Chuan Peking Univ Sch Earth & Space Sci Beijing 100871 Peoples R China Natl Supercomp Ctr Shenzhen Shenzhen 518055 Peoples R China

The phase arrival picking of the downhole microseismic dataset is a critical step in fracturing monitoring data processing. Recently, data-driven methods have been widely used in seismology studies, especially in seismic phase picking. The picking results heavily depend on whether large quantities of accurately labeled phase samples could be obtained to extract the characteristics of seismic waveforms. Also, there is a shortcoming of poor generalization ability in dealing with the cross-source transfer scenarios. In this paper, we propose a novel deep transfer learning method for microseismic phase arrival picking by fine-tuning one existing pretrained model based on a few phase samples. The pretrained model, which has been domain-adapted for phase picking, adopts 2d u-net to both extract time and space features, thereby improving the overall picking accuracy. Moreover, the fully convolutional u-net architecture has the ability to handle samples with variable sizes so could be used for bridging downhole microseismic data from different sources. The results of two transfer cases show that compared with the direct application of the pretrained model and a newly trained model, the proposed method could provide more satisfactory performance with only limited seismic phase samples. Also, our method significantly reduces the cost of labeling and saves time because of avoiding repeated training.

关键词： deep transfer learning downhole microseismic phase arrival picking 2d u-net

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utility of a novel integrated deep convolutional neural network for the segmentation of hip joint from computed tomography images in the preoperative planning of total hip arthroplasty

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JOuRNAL OF ORTHOPAEdIC SuRGERY ANd RESEARCH 2022年第1期17卷 164-164页

作者： Wu, dong Zhi, Xin Liu, Xingyu Zhang, Yiling Chai, Wei Peoples Liberat Army Gen Hosp Med Ctr 4 Senior Dept Orthoped Beijing Peoples R China Gen Hosp Chinese PLA Natl Clin Res Ctr Orthoped Sports Med & Rehabil Beijing Peoples R China Longwood Valley Med Technol Co Ltd Beijing Peoples R China Tsinghua Univ Sch Life Sci Beijing Peoples R China Tsinghua Shenzhen Int Grad Sch Inst Biomed & Hlth Engn iBHF Beijing Peoples R China

Purpose Preoperative three-dimensional planning is important for total hip arthroplasty. To simulate the placement of joint implants on computed tomography (CT), pelvis and femur must be segmented. Accurate and rapid segmentation of the hip joint is challenging. This study aimed to develop a novel deep learning network, named Changmugu net (CMG net), which could achieve accurate segmentation of the femur and pelvis. Methods The overall deep neural network architecture of CMG net employed three interrelated modules. CMG net included the 2d u-net to separate the bony and soft tissues. The modular hierarchy method was used for the main femur segmentation to achieve better performance. A layer classifier was adopted to localise femur layers among a series of CT scan images. The first module was a modified 2d u-net, which separated bony and soft tissues;it provided intermediate supervision for the main femur segmentation. The second module was the main femur segmentation, which was used to distinguish the femur from the acetabulum. The third module was the layer classifier, which served as a post-processor for the second module. Results There was a much greater overlap in accuracy results with the "gold standard" segmentation than with competing networks. The dice overlap coefficient was 93.55% +/- 5.57%;the mean surface distance was 1.34 +/- 0.24 mm, and the Hausdorff distance was 4.19 +/- 1.04 mm in the normal and diseased hips, which indicated greater accuracy than the other four competing networks. Moreover, the mean segmentation time of CMG net was 25.87 +/- 2.73 s, which was shorter than the times of the other four networks. Conclusions The prominent segmentation accuracy and run-time of CMG net suggest that it is a reliable method for clinicians to observe anatomical structures of the hip joints, even in severely diseased cases.

关键词： Computed tomography deep neural network Image segmentation Hip joint 2d u-net

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Automated Cardiovascular Pathology Assessment using Semantic Segmentation and Ensemble Learning

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JOuRNAL OF dIGITAL IMAGING 2020年第3期33卷 607-612页

作者： Lindsey, Tony Lee, Jin-Ju NASA Intelligent Syst Ames Res Ctr Room 250 Mountain View CA 94035 USA Stanford Sch Med Biomed Informat X-2151265 Welch RdMC 5479 Stanford CA 94035 USA

Cardiac magnetic resonance imaging provides high spatial resolution, enabling improved extraction of important functional and morphological features for cardiovascular disease staging. Segmentation of ventricular cavities and myocardium in cardiac cine sequencing provides a basis to quantify cardiac measures such as ejection fraction. A method is presented that curtails the expense and observer bias of manual cardiac evaluation by combining semantic segmentation and disease classification into a fully automatic processing pipeline. The initial processing element consists of a robust dilated convolutional neural network architecture for voxel-wise segmentation of the myocardium and ventricular cavities. The resulting comprehensive volumetric feature matrix captures diagnostic clinical procedure data and is utilized by the final processing element to model a cardiac pathology classifier. Our approach evaluated anonymized cardiac images from a training data set of 100 patients (4 pathology groups, 1 healthy group, 20 patients per group) examined at the university Hospital of dijon. The top average dice index scores achieved were 0.940, 0.886, and 0.849 for structure segmentation of the left ventricle (LV), myocardium, and right ventricle (RV), respectively. A 5-ary pathology classification accuracy of 90% was recorded on an independent test set using the trained model. Performance results demonstrate the potential for advanced machine learning methods to deliver accurate, efficient, and reproducible cardiac pathological assessment.

关键词： 2d u-net Cardiac cine-MRI Semantic segmentation Feature selection Classification

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Prostate lesion segmentation in MR images using radiomics based deeply supervised u-net

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BIOCYBERnetICS ANd BIOMEdICAL ENGINEERING 2020年第4期40卷 1421-1435页

作者： Hambarde, Praful Talbar, Sanjay Mahajan, Abhishek Chavan, Satishkumar Thakur, Meenakshi Sable, Nilesh SGGS Inst Engn & Technol Nanded Maharashtra India Tata Mem Hosp Mumbai 400012 Maharashtra India Don Bosco Inst Technol Mumbai Maharashtra India

Prostate lesion detection in an axial T2 weighted (T2W) MR images is a very challenging task due to heterogeneous and inconsistent pixel representation surrounding the prostate boundary. In this paper, a radiomics based deeply supervised u-net is proposed for both prostate gland and prostate lesion segmentation. The proposed pipeline is trained and validated on 1174 and 2071 T2W MR images of 40 patients and tested on 250 and 415 T2W MR images of 10 patients for prostate capsule segmentation and prostate lesion segmentation, respectively. Effective segmentation of prostate lesions in various stages of prostate cancer (namely T1, T2, T3, and T4) is achieved using the proposed framework. The mean dice Similarity Coefficient (dSC) for actual prostate capsule segmentation and prostate lesion segmentation is 0.8958 and 0.9176, respectively. The proposed framework is also tested on Promise12 public dataset for performance analysis in segmenting prostate gland. The segmentation results using proposed architecture are promising compared to state-ofthe-art techniques. It also improves the accuracy of the prostate cancer diagnosis. (c) 2020 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.

关键词： Prostate capsule segmentation Prostate lesion segmentation Radiomics 2d u-net Convolutional Neural network deep learning

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distortion correction of single-shot EPI enabled by deep-learning

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NEuROIMAGE 2020年 221卷 117170-117170页

作者： Hu, Zhangxuan Wang, Yishi Zhang, Zhe Zhang, Jieying Zhang, Huimao Guo, Chunjie Sun, Yuejiao Guo, Hua Tsinghua Univ Ctr Biomed Imaging Res Sch Med Dept Biomed Engn Beijing Peoples R China Philips Healthcare Beijing Peoples R China Capital Med Univ Beijing Tiantan Hosp China Natl Clin Res SCtr Neurol Dis Beijing Peoples R China First Hosp Jilin Univ Dept Radiol Changchun Jilin Peoples R China

Purpose: A distortion correction method for single-shot EPI was proposed. Point-spread-function encoded EPI (PSF-EPI) images were used as the references to correct traditional EPI images based on deep neural network. Theory and methods: The PSF-EPI method can obtain distortion-free echo planar images. In this study, a 2d u-net based network was trained to achieve the distortion correction of single-shot EPI (SS-EPI) images, using PSF-EPI images as targets in the training stage. Anatomical T2W-TSE images were also fed into the network to improve the quality of the results. The applications in diffusion-weighted images were used as examples in this work. The network was trained on data acquired on healthy volunteers and tested on data of both healthy volunteers and patients. The corrected EPI images from the proposed method were also compared with those from field-mapping and top-up based distortion correction methods. Results: Experimental results showed that the proposed method can correct for EPI distortions better than both the field-mapping and top-up based methods, and the results were close to the distortion-free images from PSF-EPI. Additionally, inclusion of T2W-TSE images helped improve distortion correction of the SS-EPI images without contaminating the output noticeably. The experiments with patients and different MRI platforms demonstrated the generalization feasibility of the proposed method preliminarily. Conclusion: Through the correction of diffusion-weighted images, the proposed deep-learning based method was demonstrated to have the feasibility to correct for the distortion of EPI images.

关键词： Single-shot EPI Point-spread-function encoded EPI distortion correction deep-learning 2d u-net

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