SegNet-based left ventricular MRI segmentation for the diagnosis of cardiac hypertrophy and myocardial infarction

Yan, Zhisheng; Su, Yujing; Sun, Hongwen; Yu, Haiyang; Ma, Wanteng; Chi, Honghui; Cao, Hongxin; Chang, Qing

doi:10.1016/j.cmpb.2022.107197

Cited by 9 publications

(2 citation statements)

References 28 publications

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“…This leads to incomplete restoration of such information during up-sampling, thereby impacting the accuracy of segmentation. The pooling function in SegNet ( 49 ), the skip connections between up-sampling and down-sampling in U-Net ( 41 ), 3D U-Net ( 46 ), and U-Net++ ( 48 ), and the fully connected conditional random field in DeepLab ( 45 ) are all designed to complement the detailed information during up-sampling operations. Moreover, integrating the semantic information extracted by CNN with local/global features is imperative for achieving accurate object segmentation across diverse scenes and varying sizes.…”

Section: Commonly Dl-based Algorithm For Image Semantic Segmentationmentioning

confidence: 99%

Deep learning for MRI lesion segmentation in rectal cancer

Yang,

Yang

et al. 2024

Front. Med.

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Rectal cancer (RC) is a globally prevalent malignant tumor, presenting significant challenges in its management and treatment. Currently, magnetic resonance imaging (MRI) offers superior soft tissue contrast and radiation-free effects for RC patients, making it the most widely used and effective detection method. In early screening, radiologists rely on patients’ medical radiology characteristics and their extensive clinical experience for diagnosis. However, diagnostic accuracy may be hindered by factors such as limited expertise, visual fatigue, and image clarity issues, resulting in misdiagnosis or missed diagnosis. Moreover, the distribution of surrounding organs in RC is extensive with some organs having similar shapes to the tumor but unclear boundaries; these complexities greatly impede doctors’ ability to diagnose RC accurately. With recent advancements in artificial intelligence, machine learning techniques like deep learning (DL) have demonstrated immense potential and broad prospects in medical image analysis. The emergence of this approach has significantly enhanced research capabilities in medical image classification, detection, and segmentation fields with particular emphasis on medical image segmentation. This review aims to discuss the developmental process of DL segmentation algorithms along with their application progress in lesion segmentation from MRI images of RC to provide theoretical guidance and support for further advancements in this field.

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Section: Commonly Dl-based Algorithm For Image Semantic Segmentationmentioning

confidence: 99%

Deep learning for MRI lesion segmentation in rectal cancer

Yang,

Yang

et al. 2024

Front. Med.

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“…U-Net [25] solved the problem of training small datasets by encoding-decoding U-shaped structures and extended the research of many models with good segmentation effects, such as UNet++, Attention U-Net, etc. SegNet follows the U-shaped structure and adds the max-pooling operation, which reduces the number of parameters for end-to-end training and can be more easily merged into other U-shaped structures [26].…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Effective Class Balanced Approach for Remote Sensing Imagery Semantic Segmentation of Imbalanced Data

et al. 2023

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The wide application and rapid development of satellite remote sensing technology have put higher requirements on remote sensing image segmentation methods. Because of its characteristics of large image size, large data volume, and complex segmentation background, not only are the traditional image segmentation methods difficult to apply effectively, but the image segmentation methods based on deep learning are faced with the problem of extremely unbalanced data between categories. In order to solve this problem, first of all, according to the existing effective sample theory, the effective sample calculation method in the context of semantic segmentation is firstly proposed in the highly unbalanced dataset. Then, a dynamic weighting method based on the effective sample concept is proposed, which can be applied to the semantic segmentation of remote sensing images. Finally, the applicability of this method to different loss functions and different network structures is verified on the self-built Landsat8-OLI remote sensing image-based tri-classified forest fire burning area dataset and the LoveDA dataset, which is for land-cover semantic segmentation. It has been concluded that this weighting algorithm can enhance the minimal-class segmentation accuracy while ensuring that the overall segmentation performance in multi-class segmentation tasks is verified in two different semantic segmentation tasks, including the land use and land cover (LULC) and the forest fire burning area segmentation In addition, this proposed method significantly improves the recall of forest fire burning area segmentation by as much as about 30%, which is of great reference value for forest fire research based on remote sensing images.

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Automatic segmentation of spine x‐ray images based on multiscale feature enhancement network

Du,

Liu,

Fei

et al. 2024

Medical Physics

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BackgroundAutomatic segmentation of vertebrae in spinal x‐ray images is crucial for clinical diagnosis, case analysis, and surgical planning of spinal lesions.PurposeHowever, due to the inherent characteristics of x‐ray images, including low contrast, high noise, and uneven grey scale, it remains a critical and challenging problem in computer‐aided spine image analysis and disease diagnosis applications.MethodsIn this paper, a Multiscale Feature Enhancement Network (MFENet), is proposed for segmenting whole spinal x‐ray images, to aid doctors in diagnosing spinal‐related diseases. To enhance feature extraction, the network incorporates a Dual‐branch Feature Extraction Module (DFEM) and a Semantic Aggregation Module (SAM). The DFEM has a parallel dual‐branch structure. The upper branch utilizes multiscale convolutional kernels to extract features from images. Employing convolutional kernels of different sizes helps capture details and structural information at different scales. The lower branch incorporates attention mechanisms to further optimize feature representation. By modeling the feature maps spatially and across channels, the network becomes more focused on key feature regions and suppresses task‐irrelevant information. The SAM leverages contextual semantic information to compensate for details lost during pooling and convolution operations. It integrates high‐level feature information from different scales to reduce segmentation result discontinuity. In addition, a hybrid loss function is employed to enhance the network's feature extraction capability.ResultsIn this study, we conducted a multitude of experiments utilizing dataset provided by the Spine Surgery Department of Henan Provincial People's Hospital. The experimental results indicate that our proposed MFENet demonstrates superior segmentation performance in spinal segmentation on x‐ray images compared to other advanced methods, achieving 92.61 ± 0.431 for MIoU, 92.42 ± 0.329 for DSC, and 99.51 ± 0.037 for Global_accuracy.ConclusionsOur model is able to more effectively learn and extract global contextual semantic information, significantly improving spinal segmentation performance, further aiding doctors in analyzing patient conditions.

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SegNet-based left ventricular MRI segmentation for the diagnosis of cardiac hypertrophy and myocardial infarction

Cited by 9 publications

References 28 publications

Deep learning for MRI lesion segmentation in rectal cancer

Deep learning for MRI lesion segmentation in rectal cancer

A Dynamic Effective Class Balanced Approach for Remote Sensing Imagery Semantic Segmentation of Imbalanced Data

Automatic segmentation of spine x‐ray images based on multiscale feature enhancement network

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