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17th International Conference on Advanced Computer Theory and Engineering

作者： Xu, ZhenXiong Xiao, HuaiMeng Wang, Ya Gao, WenJing Fuyang Normal Univ Sch Comp & Informat Engn Anhui Peoples R China

ISBN: (纸本)9798331539894;9798331539887

To solve the problem that UNet performs poorly in multi-class segmentation on large datasets, this paper proposes a new neural network model, B-UNet. UNet has some problems, such as poor feature processing method and large splicing span of features, leading to its poor feature extraction ability for complex images. Although UNet++ solves the problem of large feature span, the number of parameters in the model is greatly increased. Based on UNet, the B-UNet model obtained by introducing bridge features not only solves the problem that UNet has poor performance in multi-class segmentation on large datasets, but also reduces the number of parameters compared with UNet++. In order to make the introduced bridge features play a greater role in the process of feature splicing, B-UNet model introduces an attention mechanism, so that the model can focus more on the features that contribute more to the segmentation, and the reference of residual module also enhances the stability of the model. On VOC2007 dataset, MIoU, MPA and Accuracy were used as evaluation indexes, Experimental results show that B-UNet model is more suitable for multi-class segmentation on larger datasets than UNet model.

关键词： neural network multi-class segmentation bridge feature residual module attention mechanism

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multi-class segmentation of Dark-Grown Arabidopsis Seedlings with U-Net 11

Multi-class Segmentation of Dark-Grown Arabidopsis Seedlings...

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11th International Conference on Bioinformatics and Computational Biology (ICBCB)

作者： Tong, Haonan Bullock, David Schmittling, Selene Haque, Samiul Tompkins, Vincent Chen, Hao Zhang, Chuang Stepanova, Anna N. Alonso, Jose M. Williams, Cranos M. Inspur Elect Informat Ind Co Ltd Beijing 100094 Peoples R China North Carolina State Univ Dept Plant & Microbial Biol Raleigh NC 27695 USA North Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA

ISBN: (纸本)9798350397871

Plant organ phenotyping represents a powerful tool to investigate the effects of biotic and abiotic factors on plant growth and development. Phenotyping typically involves the initial capture of high-resolution images of the plant of interest and the subsequent measurement of different morphometric parameters of specific plant organs. This second step is typically very time consuming and difficult to automate. To deal with this bottleneck, we developed a multi-class segmentation model based on U-shape network to identify hypocotyls and roots of young seedlings of the reference plant Arabidopsis thaliana. We applied a balanced cross entropy loss function to learn an alternative optimal network structure for this multi-class segmentation task. We evaluated our segmentation machine using 66 images of the wild-type Arabidopsis strain Col-0, as well as 34 images of the highly agravitropic and morphologically distinct mutant strain, aux1-7. Our model achieved a mean BFscore of 0.81 for Col-0 seedlings and 0.75 for aux1-7 mutant seedlings on the test dataset. Our model was also able to maintain accuracy in these two morphologically different genotypes suggesting that our segmentation procedure could be successfully applied to Arabidopsis seedlings showing broad morphological differences due to their genotype or treatment conditions. Appropriate segmentation is the first step in identifying phenotypic changes under hormone-mediated stress response. The identified growth parameters will be useful to identify the response associated with both abiotic and biotic stresses, which include but not limited to drought stress, heat stress, and the presence of pests or pathogens. The quantification of these parameters will aid assessment of genetic factors that contribute to the stress response.

关键词： multi-class segmentation plant phenotype U-Net encoder-decoder deep learning

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A novel 3D differential entropy-based multi-class segmentation technique for brain MR image analysis

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2025年 100卷

作者： Samantaray, Leena Tripathy, Tanmaya Tapaswini Panda, Rutuparna Ajay Binay Inst Technol Dept Elect & Commun Engn Cuttack 753014 Orissa India Veer Surendra Sai Univ Technol Dept Elect & Telecommun Engn Burla 768018 Odisha Romania

Objective: Two-dimension (2-D) histograms are constructed using the average neighbourhood pixel intensities. Nevertheless, the procedure of averaging leads to the loss of information. Further, 2-D methods do not perform well for the low spectral medical images. Therefore, there is a need for three-dimension (3-D) extensions. This research gap is addressed in this article. Methods: A novel idea of a 3-D differential entropy-based methodology for multi-class segmentation is being investigated, which is the primary contribution. The novelty of the work is the derivation of firsthand objective functions in a 3-D setting. The investigation is focused on a 3-D computational model that estimates differential entropy. In addition, a hybridized optimizer named Equilibrium Optimization with LeAvy Flight (EOL) has been proposed as the secondary contribution. The EOL is used to maximize the suggested objective functions. Results: Extensive experiments are conducted using the brain MR images from Harvard Medical School database. The method preserves more information. An increased accuracy is attained. Conclusion: The statistical data analysis reveals that the suggested methodology is better than the state-of-the-art techniques. It is well suited for low contrast medical image analysis. Significance: The 3-D model investigated in this paper is unique. The future applications of the method would be multiclass segmentation of various MR contrasts/modalities useful for medical image analysis.

关键词： Brain Magnetic Resonance Imaging Analysis Artificial Intelligence Entropy multi-class segmentation Equilibrium Optimizer Flight

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Uncertainty quantification in multi-class segmentation: Comparison between Bayesian and non-Bayesian approaches in a clinical perspective

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MEDICAL PHYSICS 2024年第9期51卷 6090-6102页

作者： Scalco, Elisa Pozzi, Silvia Rizzo, Giovanna Lanzarone, Ettore Natl Res Council CNR Inst Biomed Technol ITB Milan Italy Univ Bergamo Dept Management Informat & Prod Engn Bergamo Italy Natl Res Council CNR Inst Intelligent Ind Technol & Syst STIIMA Milan Italy Natl Res Council ITB CNR Inst Biomed Technol Via Fratelli Cervi 93 I-20054 Segrate MI Italy

BackgroundAutomatic segmentation techniques based on Convolutional Neural Networks (CNNs) are widely adopted to automatically identify any structure of interest from a medical image, as they are not time consuming and not subject to high intra- and inter-operator variability. However, the adoption of these approaches in clinical practice is slowed down by some factors, such as the difficulty in providing an accurate quantification of their *** work aims to evaluate the uncertainty quantification provided by two Bayesian and two non-Bayesian approaches for a multi-class segmentation problem, and to compare the risk propensity among these approaches, considering CT images of patients affected by renal cancer (RC).MethodsFour uncertainty quantification approaches were implemented in this work, based on a benchmark CNN currently employed in medical image segmentation: two Bayesian CNNs with different regularizations (Dropout and DropConnect), named BDR and BDC, an ensemble method (Ens) and a test-time augmentation (TTA) method. They were compared in terms of segmentation accuracy, using the Dice score, uncertainty quantification, using the ratio of correct-certain pixels (RCC) and incorrect-uncertain pixels (RIU), and with respect to inter-observer variability in manual segmentation. They were trained with the Kidney and Kidney Tumor segmentation Challenge launched in 2021 (Kits21), for which multi-class segmentations of kidney, RC, and cyst on 300 CT volumes are available. Moreover, they were tested considering this and other two public renal CT *** results achieved large differences across the structures of interest for all approaches, with an average Dice score of 0.92, 0.58, and 0.21 for kidney, tumor, and cyst, respectively. In terms of uncertainties, TTA provided the highest uncertainty, followed by Ens and BDC, whereas BDR provided the lowest, and minimized the number of incorrect certain pixels worse than the other approa

关键词： Bayesian convolutional neural network Monte Carlo dropout and dropconnect multi-class segmentation uncertainty quantification risk propensity degree

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multi-scale Perception and Feature Refinement Network for multi-class segmentation of intracerebral hemorrhage in CT images

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年 88卷

作者： Xiao, Ye Hou, Yuanyuan Wang, Zhiqiang Zhang, Yuan Li, Xuanya Hu, Kai Gao, Xieping Xiangtan Univ Key Lab Intelligent Comp & Informat Proc Minist Educ Xiangtan 411105 Peoples R China Xiangnan Univ Key Lab Med Imaging & Artificial Intelligence Huna Chenzhou 423000 Peoples R China Xiangnan Univ Coll Med Imaging Lab & Rehabil Chenzhou 423000 Peoples R China Baidu Inc Beijing 100085 Peoples R China Hunan Normal Univ Hunan Prov Key Lab Intelligent Comp & Language Inf Changsha 410081 Peoples R China

Intracerebral hemorrhage (ICH) poses a severe threat to human health and well-being. Automatic segmentation of hematomas in CT images can provide essential diagnostic assistance to physicians, and ensure improved treatment and recovery outcomes for patients. Existing methods for intracerebral hemorrhage segmentation mainly focus on identifying hemorrhage areas, without the ability to accurately distinguish and outline different types of hematomas. However, different types of hemorrhage exhibit a high degree of similarity in terms of gray matter level and shape, and the scale of hematomas can vary significantly. To address this issue, we propose a multi-scale Perception and Feature Refinement Network (MPFR-Net) for automatic segmentation of both intraparenchymal and intraventricular hemorrhages. Specifically, we propose a multi-scale Perception Module (MPM), which consists of the integration of features at different levels and the local and global multi-scale branches. MPM allows for the efficient extraction of multi-scale features and the establishment of long-range relationships between the target and background. Additionally, we propose a Feature Refinement Module (FRM) to refine fuzzy detail information that has been lost after down-sampling to the deep layer, while simultaneously supplementing the small target information from shallow features. To improve the clinical adaptability of our method, we further collect 608 patient cases from multiple hospitals to construct a multi-center dataset, termed as ICH-Seg in which each case contains both intraparenchymal and intraventricular hemorrhages. From the quantitative and visual results, MPFR-Net outperforms previous methods on both private and public datasets, showing promising segmentation for intracerebral hemorrhage and potential clinical applications.

关键词： Intracerebral hemorrhage CT images multi-class segmentation multi-scale perception Feature refinement

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SPA-RESUNET: STRIP POOLING ATTENTION RESUNET FOR multi-class segmentation OF VERTEBRAE AND INTERVERTEBRAL DISCS 19

SPA-RESUNET: STRIP POOLING ATTENTION RESUNET FOR MULTI-CLASS...

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19th IEEE International Symposium on Biomedical Imaging (IEEE ISBI)

作者： Li, Chuanpu Liu, Tianbao Chen, Zeli Pang, Shumao Zhong, Liming Feng, Qianjin Yang, Wei Southern Med Univ Sch Biomed Engn Guangzhou Peoples R China Southern Med Univ Guangdong Prov Key Lab Med Image Proc Guangzhou Peoples R China Southern Med Univ Guangdong Prov Engn Lab Med Imaging & Diagnost Te Guangzhou Peoples R China

ISBN: (纸本)9781665429238

Automated multi-class segmentation of vertebrae and intervertebral discs for volumetric magnetic resonance image(MRI) can help the diagnosis and the treatment of many spinal diseases. However, existing methods based on fully convolutional network mostly stacked local convolution and pooling operations, and thus failed to capture the long-range dependencies for spine segmentation. In this paper, we propose a novel and computation-efficient residual U-Net with strip-pooling attention mechanism (SPA-ResUNet) for the multi-class segmentation of vertebrae and intervertebral discs. Due to the effectiveness of skip connections in U-Net, the strip pooling attention module is adopted in the skip connections to learn the long-range dependencies for improving the segmentation performance of the top structures and sacral crest of spine. Our SPA-ResUNet was implemented on the MRSpineSeg Challenge dataset. Experiments show that our SPA-ResUNet achieves impressive performance with mean Dice similarity coefficients of 88.14% and 87.88% in all 19 spinal structures on two test datasets, respectively. The visual segmentation results also show that our method achieves superior segmentation in top structures and sacral crest of spine. The proposed SPA-ResUNet has high potential in aiding the diagnosis and the treatment of spinal diseases.

关键词： Spine multi-class segmentation Deep Learning Strip Pooling Attention

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Modeling multi-annotator Uncertainty as multi-class segmentation Problem 7th

Modeling Multi-annotator Uncertainty as Multi-class Segmenta...

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7th International Brain Lesion Workshop (BrainLes)

作者： Zukovec, Martin Dular, Lara Spiclin, Ziga Univ Ljubljana Fac Elect Engn Trzaska Cesta 25 Ljubljana 1000 Slovenia

ISBN: (纸本)9783031089992;9783031089985

Medical image segmentation is a monotonous, time-consuming, and costly task performed by highly skilled medical annotators. Despite adequate training, the intra- and inter-annotator variations results in significantly differing segmentations. If the variations arise from the uncertainty of the segmentation task, due to poor image contrast, lack of expert consensus, etc., then the algorithms for automatic segmentation should learn to capture the annotator (dis)agreements. In our approach we modeled the annotator (dis)agreement by aggregating the multi-annotator segmentations to reflect the uncertainty of the segmentation task and formulated the segmentation as multi-class pixel classification problem within an open source convolutional neural architecture nnU-Net. Validation was carried out for a wide range of imaging modalities and segmentation tasks as provided by the 2020 and 2021 QUBIQ (Quantification of Uncertainties in Biomedical Image Quantification) challenges. We achieved high quality segmentation results, despite a small set of training samples, and at time of this writing achieved an overall third and sixth best result on the respective QUBIQ 2020 and 2021 challenge leaderboards.

关键词： multi-class segmentation Noisy labels Uncertainty aggregation Convolutional neural networks Challenge datasets

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Unpaired deep adversarial learning for multi-class segmentation of instruments in robot-assisted surgical videos

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INTERNATIONAL JOURNAL OF MEDICAL ROBOTICS AND COMPUTER ASSISTED SURGERY 2023年第4期19卷 e2514-e2514页

作者： Nema, Shubhangi Vachhani, Leena Indian Inst Technol Syst & Control Grp Mumbai 470001 Maharastra India

PurposeImage segmentation of instruments in the raw surgical videos is a critical component of intraoperative assistance softwares. Challenges include addressing rendered overlays occluding the instrument while providing pivotal input to instrument tracking frameworks and, train the segmentation process with limited labelled data available from surgical videos. MethodThe proposed adversarial network, InstruSegNet uses unpaired training (eliminating need for massive paired data) for automated multi-class surgical instrument segmentation in raw surgical videos with complex backgrounds. The proposed method is applied for single/multiple robotic and rigid instruments and optimised on least square Generative Adversarial Networks loss. ResultPromising validation has been conducted on the publicly available dataset. Proposed approach for multi-class segmentation of robotic and rigid instruments meets outstanding performance in terms of accuracy and surpasses the existing methods. ApplicationThis work facilitates segmenting instrument information without manual interventions from raw videos providing means to code surgeon's actions for developing intelligent assistance software.

关键词： Generative Adversarial Network (GAN) multi-class segmentation robot-assisted surgery surgical instruments unpaired training

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segmentation of retinal detachment and retinoschisis in OCT images based on complementary multi-class segmentation networks

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PHYSICS IN MEDICINE AND BIOLOGY 2023年第11期68卷 115019-115019页

作者： Shi, Fei Yang, Changqing Jiang, Qingxin Zhu, Weifang Xu, Xun Chen, Xinjian Fan, Ying Soochow Univ Sch Elect & Informat Engn MIPAV Lab Suzhou 215006 Peoples R China Shanghai Jiao Tong Univ Peoples Hosp 1 Shanghai 200080 Peoples R China Soochow Univ State Key Lab Radiat Med & Protect Suzhou 215123 Peoples R China

Retinal detachment (RD) and retinoschisis (RS) are the main complications leading to vision loss in high myopia. Accurate segmentation of RD and RS, including its subcategories (outer, middle, and inner retinoschisis) in optical coherence tomography images is of great clinical significance in the diagnosis and management of high myopia. For this multi-class segmentation task, we propose a novel framework named complementary multi-class segmentation networks. Based on domain knowledge, a three-class segmentation path (TSP) and a five-class segmentation path (FSP) are designed, and their outputs are integrated through additional decision fusion layers to achieve improved segmentation in a complementary manner. In TSP, a cross-fusion global feature module is adopted to achieve global receptive field. In FSP, a novel three-dimensional contextual information perception module is proposed to capture long-range contexts, and a classification branch is designed to provide useful features for segmentation. A new category loss is also proposed in FSP to help better identify the lesion categories. Experiment results show that the proposed method achieves superior performance for joint segmentation of RD and the three subcategories of RS, with an average Dice coefficient of 84.83%.

关键词： multi-class segmentation OCT image deep learning retinal detachment retinoschisis

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multi-class segmentation of skin lesions via joint dictionary learning

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2021年第0期68卷 102787-102787页

作者： Moradi, Nooshin Mandavi-Amiri, Nezam Sharif Univ Technol Fac Math Sci Tehran Iran

Melanoma is the deadliest type of human skin cancer. However, it is curable if diagnosed in an early stage. Recently, computer aided diagnosis (CAD) systems have drawn much interests. segmentation is a crucial step of a CAD system. There are different types of skin lesions having high similarities in terms of color, shape, size and appearance. Most available works focus on a binary segmentation. Due to the huge variety of skin lesions and high similarities between different types of lesions, multi-class segmentation is still a challenging task. Here, we propose a method based on joint dictionary learning for multi-class segmentation of dermoscopic images. The key idea is based on combining data from different feature spaces to build a more informative structure. We consider training data from two different spaces. Then, two dictionaries are jointly learned using the K-SVD algorithm. The final segmentation is accomplished by a graph-cut method based on both the topological infor-mation of lesions and the learned dictionaries. We evaluate our proposed method on the ISIC 2107 dataset to segment three classes of lesions. Our method achieves better results, specially for challenging skin lesions, compared to the only available method for multi-class segmentation of dermoscopic images. We also evaluate the performance of our method for binary segmentation and lesion diagnosis and compared the results with the other state-of-the-art methods. Experimental results show the efficiency and effectiveness of the proposed method in producing results that are more reliable for clinical applications, even using limited amount of training data.

关键词： Sparse representation Joint dictionary learning multi-class segmentation Skin lesion Graph-cuts

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