FFUNet: A novel feature fusion makes strong decoder for medical image segmentation

Xie, Junsong; Zhu, Renju; Wu, Zezhi; Ouyang, Jinling

doi:10.1049/sil2.12114

Cited by 16 publications

(9 citation statements)

References 61 publications

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“…The concept of incorporating Transformer modules into the network architecture of U-Net has reignited a research wave centered around Transformer-based approaches in the domain of medical image segmentation. On one hand, most researchers have been exploring how to embed serial Transformer modules into the U-Net structure, leading to a series of classic networks such as TransUNet [5], Swin-Unet [6], UNETR [7] and so on [8][9][10][11][12]. Undeniably, serial Transformer network models have significantly improved the accuracy of medical image segmentation.…”

Section: Introductionmentioning

confidence: 99%

Cross-Parallel Transformer: Parallel ViT for Medical Image Segmentation

Wang,

Chen

et al. 2023

Sensors

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Medical image segmentation primarily utilizes a hybrid model consisting of a Convolutional Neural Network and sequential Transformers. The latter leverage multi-head self-attention mechanisms to achieve comprehensive global context modelling. However, despite their success in semantic segmentation, the feature extraction process is inefficient and demands more computational resources, which hinders the network’s robustness. To address this issue, this study presents two innovative methods: PTransUNet (PT model) and C-PTransUNet (C-PT model). The C-PT module refines the Vision Transformer by substituting a sequential design with a parallel one. This boosts the feature extraction capabilities of Multi-Head Self-Attention via self-correlated feature attention and channel feature interaction, while also streamlining the Feed-Forward Network to lower computational demands. On the Synapse public dataset, the PT and C-PT models demonstrate improvements in DSC accuracy by 0.87% and 3.25%, respectively, in comparison with the baseline model. As for the parameter count and FLOPs, the PT model aligns with the baseline model. In contrast, the C-PT model shows a decrease in parameter count by 29% and FLOPs by 21.4% relative to the baseline model. The proposed segmentation models in this study exhibit benefits in both accuracy and efficiency.

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Section: Introductionmentioning

confidence: 99%

Cross-Parallel Transformer: Parallel ViT for Medical Image Segmentation

Wang,

Chen

et al. 2023

Sensors

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“…Among these, CNN emerges as one of the prevalent DL models, extensively employed in tasks such as MIS, classification, and reconstruction. By leveraging extensive training on voluminous medical image datasets, CNN autonomously learns feature representations within images, enabling precise processing and analysis of medical imagery [9]. DL models, such as U-Net and FCN, have achieved excellent results in MIS tasks, and can accurately identify different tissue structures or lesion areas in images.…”

Section: Challenges Of Mis and 3d Reconstructionmentioning

confidence: 99%

Research on the Application of Deep Learning in Medical Image Segmentation and 3D Reconstruction

Zi,

Wang,

Gao

et al. 2024

AJST

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Medical image segmentation (MIS) and 3D reconstruction are crucial research directions in the field of medical imaging, which is of great significance for disease diagnosis, treatment planning and surgical navigation. In recent years, with the rapid development of Deep Learning (DL) technology, DL has made remarkable progress in the field of medical image processing and has become one of the important methods of MIS and 3D reconstruction. In this paper, the application of DL technologies in MIS and 3D reconstruction is systematically studied and discussed. Firstly, the paper introduces the basic concepts and technical challenges of MIS and 3D reconstruction, including image quality, noise interference and edge detection. Secondly, the paper introduces the data acquisition process in detail, including the medical image data set and data preprocessing method. Then, the paper puts forward the DL model framework based on self-attention mechanism, as well as the loss function and optimizer used in the training process. Then, the model is verified by experiments, and the performance of different models in MIS and 3D reconstruction is analyzed. Finally, the experimental results are comprehensively analyzed, and the application prospect and future development direction of DL in MIS and 3D reconstruction are discussed. The research results of this paper provide important theoretical and practical guidance for improving medical image processing technology and promoting the development and clinical application of medical imaging.

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“…In medical image segmentation, there are also related studies on deformable convolution. Xie et al [20] propose a feature fusion module for medical image segmentation. The module consists of feature attention selection, cross-offset generation, and deformable convolution layers to alleviate the ambiguous semantic information between the encoder and decoder.…”

Section: Work On Deformable Convolutionmentioning

confidence: 99%

“…Real cells often have diverse shapes, which may cause deviations in shape from the circular annotations in the PSI map. In previous studies of image segmentation, some researchers have proposed using deformable convolutions to improve the segmentation results of irregular objects in images [19], [20]. The experiments indicate that using deformable convolutions can make the originally relatively regular segmentation edges more complex, thereby closer to the annotation.…”

Section: Difference Deformable Convolution Modulementioning

confidence: 99%

Learning Pseudo Scale Instance Maps for Cell Localization

Zhang¹,

Chen²,

Li³

et al. 2023

Preprint

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<p>In the field of medical image analysis, especially in the study of pathological morphology images of tumor tissues, accurately locating cells in images is a crucial task. However, this task faces multiple challenges, such as differences in cell staining intensity and cell morphology, and variance in cell shape and scale caused by manual stretching. Currently, the solution is to use fixed-scale density maps to supervise the model’s training, which makes it difficult for the model to handle cells of different scales and cell localization scenarios under different magnifications. To address this problem, this paper proposes a new cell location map called Pseudo Scale Instance (PSI) map, which has instance-level scale information. Additionally, to address the problem of significant differences between complex cell shapes and masks in PSI maps, this paper introduces a gradient alignment module based on Difference Deformable Convolution (DDC). The module uses gradient information to calculate the offsets in the deformable convolution, making the model’s output closer to the ground truth. Experiments show that the PSI map proposed in this paper can effectively utilize scale information to improve the baseline performance of cell localization and counting. Moreover, the DDC module can effectively bridge the gap between cell images and ground truth, thereby alleviating the problem of diverse cell shapes. Compared with existing methods, the proposed method has significantly improved the localization performance on two public datasets, setting a new benchmark for cell localization tasks. </p>

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FFUNet: A novel feature fusion makes strong decoder for medical image segmentation

Cited by 16 publications

References 61 publications

Cross-Parallel Transformer: Parallel ViT for Medical Image Segmentation

Cross-Parallel Transformer: Parallel ViT for Medical Image Segmentation

Research on the Application of Deep Learning in Medical Image Segmentation and 3D Reconstruction

Learning Pseudo Scale Instance Maps for Cell Localization

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