Improved U-Net Based on Mixed Loss Function for Liver Medical Image Segmentation

Yongjia, 黄泳嘉 Huang; Zaifeng, 史再峰 Shi; Zhongqi, 王仲琦 Wang; Zhe, 王哲 Wang

doi:10.3788/lop57.221003

Cited by 4 publications

(2 citation statements)

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“…Su and Fang 48 introduced a multichannel segmentation model that utilized gradient calculation to generate the training set, achieving a Dice coefficient of 0.9429. Huang et al 49 proposed transposed-resize (TR) convolution, which minimized information loss during downsampling and introduced a new loss function, leading to a Dice coefficient of 0.9514. Aslani et al 17 achieving a Dice score of 0.9554 on our test set.…”

Section: Evaluation On Chaosmentioning

confidence: 99%

PS5-Net: a medical image segmentation network with multiscale resolution

Li,

Liu,

et al. 2024

J. Med. Imag.

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.PurposeIn recent years, the continuous advancement of convolutional neural networks (CNNs) has led to the widespread integration of deep neural networks as a mainstream approach in clinical diagnostic support. Particularly, the utilization of CNN-based medical image segmentation has delivered favorable outcomes for aiding clinical diagnosis. Within this realm, network architectures based on the U-shaped structure and incorporating skip connections, along with their diverse derivatives, have gained extensive utilization across various medical image segmentation tasks. Nonetheless, two primary challenges persist. First, certain organs or tissues present considerable complexity, substantial morphological variations, and size discrepancies, posing significant challenges for achieving highly accurate segmentation. Second, the predominant focus of current deep neural networks on single-resolution feature extraction limits the effective extraction of feature information from complex medical images, thereby contributing to information loss via continuous pooling operations and contextual information interaction constraints within the U-shaped structure.ApproachWe proposed a five-layer pyramid segmentation network (PS5-Net), a multiscale segmentation network with diverse resolutions that is founded on the U-Net architecture. Initially, this network effectively leverages the distinct features of images at varying resolutions across different dimensions, departing from prior single-resolution feature extraction methods to adapt to intricate and variable segmentation scenarios. Subsequently, to comprehensively integrate feature information from diverse resolutions, a kernel selection module is proposed to assign weights to features across different dimensions, enhancing the fusion of feature information from various resolutions. Within the feature extraction network denoted as PS-UNet, we preserve the classical structure of the traditional U-Net while enhancing it through the incorporation of dilated convolutions.ResultsPS5-Net attains a Dice score of 0.9613 for liver segmentation on the CHLISC dataset and 0.8587 on the ISIC2018 dataset for skin lesion segmentation. Comparative analysis with diverse medical image segmentation methodologies in recent years reveals that PS5-Net has achieved the highest scores and substantial advancements.ConclusionsPS5-Net effectively harnesses the rich semantic information available at different resolutions, facilitating a comprehensive and nuanced understanding of the input medical images. By capitalizing on global contextual connections, the network adeptly captures the intricate interplay of features and dependencies across the entire image, resulting in more accurate and robust segmentation outcomes. The experimental validation of PS5-Net underscores its superior performance in medical image segmentation tasks, offering promising prospects for enhancing diagnostic and analytical processes within clinical settings. These results highlight the potential of PS5-Net to significantly c...

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Section: Evaluation On Chaosmentioning

confidence: 99%

PS5-Net: a medical image segmentation network with multiscale resolution

Li,

Liu,

et al. 2024

J. Med. Imag.

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show abstract

“…where x and y are denots as pixels of two pictures respectively, µ x and µ y are the mean values of x and y, and σ x and σ y are the standard deviations of x and y. C 1 is set to 0.022 and C 2 is set to 0.042 to prevent the denominator from being 0 [29]. When combined with these two loss functions, the details of the target portrait boundary can be optimized effectively in the segmentation process, thus improving the overall segmentation accuracy of the model.…”

Section: Loss Functionmentioning

confidence: 99%

Portrait Semantic Segmentation Method Based on Dual Modal Information Complementarity

Feng,

Tang

2024

Applied Sciences

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Semantic segmentation of human images is a research hotspot in the field of computer vision. At present, the semantic segmentation models based on U-net generally lack the ability to capture the spatial information of images. At the same time, semantic incompatibility exists because the feature maps of encoder and decoder are directly connected in the skip connection stage. In addition, in low light scenes such as at night, it is easy for false segmentation and segmentation accuracy to appear. To solve the above problems, a portrait semantic segmentation method based on dual-modal information complementarity is proposed. The encoder adopts a double branch structure, and uses a SK-ASSP module that can adaptively adjust the convolution weights of different receptor fields to extract features in RGB and gray image modes respectively, and carries out cross-modal information complementarity and feature fusion. A hybrid attention mechanism is used in the jump connection phase to capture both the channel and coordinate context information of the image. Experiments on human matting dataset show that the PA and MIoU coefficients of this algorithm model reach 96.58% and 94.48% respectively, which is better than U-net benchmark model and other mainstream semantic segmentation models.

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A Cascaded 3D Neural Network For Liver Tumor Segmentation

Qiu

Pei

et al. 2021

2021 IEEE International Conference on Medical Imaging Physics and Engineering (ICMIPE)

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Improved U-Net Based on Mixed Loss Function for Liver Medical Image Segmentation

Cited by 4 publications

References 0 publications

PS5-Net: a medical image segmentation network with multiscale resolution

PS5-Net: a medical image segmentation network with multiscale resolution

Portrait Semantic Segmentation Method Based on Dual Modal Information Complementarity

A Cascaded 3D Neural Network For Liver Tumor Segmentation

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