Dataset Information

MHA-Net: A Multibranch Hybrid Attention Network for Medical Image Segmentation.

ABSTRACT: The robust segmentation of organs from the medical image is the key technique in medical image analysis for disease diagnosis. U-Net is a robust structure for medical image segmentation. However, U-Net adopts consecutive downsampling encoders to capture multiscale features, resulting in the loss of contextual information and insufficient recovery of high-level semantic features. In this paper, we present a new multibranch hybrid attention network (MHA-Net) to capture more contextual information and high-level semantic features. The main idea of our proposed MHA-Net is to use the multibranch hybrid attention feature decoder to recover more high-level semantic features. The lightweight pyramid split attention (PSA) module is used to connect the encoder and decoder subnetwork to obtain a richer multiscale feature map. We compare the proposed MHA-Net to state-of-art approaches on the DRIVE dataset, the fluoroscopic roentgenographic stereophotogrammetric analysis X-ray dataset, and the polyp dataset. The experimental results on different modal images reveal that our proposed MHA-Net provides better segmentation results than other segmentation approaches.

SUBMITTER: Zhang M

PROVIDER: S-EPMC9560845 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

MHA-Net: A Multibranch Hybrid Attention Network for Medical Image Segmentation.

Zhang Meifang M Sun Qi Q Cai Fanggang F Yang Changcai C

Computational and mathematical methods in medicine 20221006

The robust segmentation of organs from the medical image is the key technique in medical image analysis for disease diagnosis. U-Net is a robust structure for medical image segmentation. However, U-Net adopts consecutive downsampling encoders to capture multiscale features, resulting in the loss of contextual information and insufficient recovery of high-level semantic features. In this paper, we present a new multibranch hybrid attention network (MHA-Net) to capture more contextual information ...[more]

PMID: 36245836

Similar Datasets

Project description:Deep convolutional neural networks (DCNN) achieve very high accuracy in segmenting various anatomical structures in medical images but often suffer from relatively poor generalizability. Multi-atlas segmentation (MAS), while less accurate than DCNN in many applications, tends to generalize well to unseen datasets with different characteristics from the training dataset. Several groups have attempted to integrate the power of DCNN to learn complex data representations and the robustness of MAS to changes in image characteristics. However, these studies primarily focused on replacing individual components of MAS with DCNN models and reported marginal improvements in accuracy. In this study we describe and evaluate a 3D end-to-end hybrid MAS and DCNN segmentation pipeline, called Deep Label Fusion (DLF). The DLF pipeline consists of two main components with learnable weights, including a weighted voting subnet that mimics the MAS algorithm and a fine-tuning subnet that corrects residual segmentation errors to improve final segmentation accuracy. We evaluate DLF on five datasets that represent a diversity of anatomical structures (medial temporal lobe subregions and lumbar vertebrae) and imaging modalities (multi-modality, multi-field-strength MRI and Computational Tomography). These experiments show that DLF achieves comparable segmentation accuracy to nnU-Net (Isensee et al., 2020), the state-of-the-art DCNN pipeline, when evaluated on a dataset with similar characteristics to the training datasets, while outperforming nnU-Net on tasks that involve generalization to datasets with different characteristics (different MRI field strength or different patient population). DLF is also shown to consistently improve upon conventional MAS methods. In addition, a modality augmentation strategy tailored for multimodal imaging is proposed and demonstrated to be beneficial in improving the segmentation accuracy of learning-based methods, including DLF and DCNN, in missing data scenarios in test time as well as increasing the interpretability of the contribution of each individual modality.

Project description:BackgroundMedical image segmentation is crucial for clinical diagnostics and treatment planning. Recently, hybrid models often neglect the local modeling capabilities of Transformers for medical image segmentation, despite the complementary nature of local information from both convolutional neural networks (CNNs) and transformers. This limitation is particularly problematic in multi-organ segmentation, where organs are closely adhered, and accurate delineation is essential. This study aims to develop a novel method that leverages the strengths of both CNNs and transformers to address the challenges of segmenting multiple closely adhered organs, improving accuracy and robustness in multi-organ segmentation.MethodsIn this study, we present the Not Another Dual Attention block (NNDA-block), a versatile, plug-and-play module that seamlessly integrates channel and spatial attention mechanisms. This block can be incorporated at any stage within the Not Another Dual Attention UNet Transformer (NNDA-UNETR) framework. Our novel approach to spatial attention uniquely combines local modeling, significantly reducing detail and texture loss during the up- and down-sampling processes typical in U-shaped architectures.ResultsEvaluations on the Beyond the Cranial Vault (BTCV) and Automatic Cardiac Diagnosis Challenge benchmark datasets demonstrate our method's effectiveness in balancing model complexity with accuracy. On the BTCV dataset, our model sets new state-of-the-art benchmarks, achieving a Dice similarity coefficient of 84.13%, normalized surface dice of 86.96%, mean average surface distance of 3.46 mm, and an Hausdorff distance at 95% of 17.76 mm. Moreover, it reduces the parameter count by 34.2% compared to leading contemporary methods, all while maintaining low computational costs [measured in floating point operations per second (FLOPs)].ConclusionsNNDA-UNETR offers a robust solution for accurate segmentation in multi-organ tasks, particularly where organ adhesion poses challenges. Its lightweight design also makes it well-suited for deployment in real-world medical environments with limited computational resources.

Dataset Information

MHA-Net: A Multibranch Hybrid Attention Network for Medical Image Segmentation.

Publications

MHA-Net: A Multibranch Hybrid Attention Network for Medical Image Segmentation.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets