PDF(Pubmed)
Abstract：
BACKGROUND: Polyp detection and localization are essential tasks for colonoscopy. U-shape network based convolutional neural networks have achieved remarkable segmentation performance for biomedical images, but lack of long-range dependencies modeling limits their receptive fields.
OBJECTIVE: Our goal was to develop and test a novel architecture for polyp segmentation, which takes advantage of learning local information with long-range dependencies modeling.
METHODS: A novel architecture combining with multi-scale nested UNet structure integrated transformer for polyp segmentation was developed. The proposed network takes advantage of both CNN and transformer to extract distinct feature information. The transformer layer is embedded between the encoder and decoder of a U-shape net to learn explicit global context and long-range semantic information. To address the challenging of variant polyp sizes, a MSFF unit was proposed to fuse features with multiple resolution.
RESULTS: Four public datasets and one in-house dataset were used to train and test the model performance. Ablation study was also conducted to verify each component of the model. For dataset Kvasir-SEG and CVC-ClinicDB, the proposed model achieved mean dice score of 0.942 and 0.950 respectively, which were more accurate than the other methods. To show the generalization of different methods, we processed two cross dataset validations, the proposed model achieved the highest mean dice score. The results demonstrate that the proposed network has powerful learning and generalization capability, significantly improving segmentation accuracy and outperforming state-of-the-art methods.
CONCLUSIONS: The proposed model produced more accurate polyp segmentation than current methods on four different public and one in-house datasets. Its capability of polyps segmentation in different sizes shows the potential clinical application.
OBJECTIVE: Our goal was to develop and test a novel architecture for polyp segmentation, which takes advantage of learning local information with long-range dependencies modeling.
METHODS: A novel architecture combining with multi-scale nested UNet structure integrated transformer for polyp segmentation was developed. The proposed network takes advantage of both CNN and transformer to extract distinct feature information. The transformer layer is embedded between the encoder and decoder of a U-shape net to learn explicit global context and long-range semantic information. To address the challenging of variant polyp sizes, a MSFF unit was proposed to fuse features with multiple resolution.
RESULTS: Four public datasets and one in-house dataset were used to train and test the model performance. Ablation study was also conducted to verify each component of the model. For dataset Kvasir-SEG and CVC-ClinicDB, the proposed model achieved mean dice score of 0.942 and 0.950 respectively, which were more accurate than the other methods. To show the generalization of different methods, we processed two cross dataset validations, the proposed model achieved the highest mean dice score. The results demonstrate that the proposed network has powerful learning and generalization capability, significantly improving segmentation accuracy and outperforming state-of-the-art methods.
CONCLUSIONS: The proposed model produced more accurate polyp segmentation than current methods on four different public and one in-house datasets. Its capability of polyps segmentation in different sizes shows the potential clinical application.
摘要：
背景：息肉检测和定位是结肠镜检查的重要任务。基于U型网络的卷积神经网络对生物医学图像取得了显著的分割性能,但是缺乏长期依赖建模限制了它们的接受领域。
目的：我们的目标是开发和测试一种新的息肉分割架构，它利用远程依赖建模来学习本地信息。
方法：开发了一种新颖的体系结构，该体系结构与多尺度嵌套UNet结构集成变压器相结合，用于息肉分割。所提出的网络利用CNN和变换器来提取不同的特征信息。转换器层嵌入在U形网的编码器和解码器之间，以学习显式的全局上下文和远程语义信息。为了解决变异息肉大小的挑战，提出了一种MSFF单元来融合具有多个分辨率的特征。
结果：使用四个公共数据集和一个内部数据集来训练和测试模型性能。还进行了消融研究以验证模型的每个组件。对于数据集Kvasir-SEG和CVC-ClinicDB，所提出的模型的平均骰子得分分别为0.942和0.950，比其他方法更准确。为了展示不同方法的概括，我们处理了两个交叉数据集验证，所提出的模型获得了最高的平均骰子得分。结果表明，所提出的网络具有强大的学习和泛化能力，显着提高分割精度，优于最先进的方法。
结论：在四个不同的公共和一个内部数据集上，所提出的模型比当前的方法产生了更准确的息肉分割。其不同大小的息肉分割能力显示了潜在的临床应用。
目的：我们的目标是开发和测试一种新的息肉分割架构，它利用远程依赖建模来学习本地信息。
方法：开发了一种新颖的体系结构，该体系结构与多尺度嵌套UNet结构集成变压器相结合，用于息肉分割。所提出的网络利用CNN和变换器来提取不同的特征信息。转换器层嵌入在U形网的编码器和解码器之间，以学习显式的全局上下文和远程语义信息。为了解决变异息肉大小的挑战，提出了一种MSFF单元来融合具有多个分辨率的特征。
结果：使用四个公共数据集和一个内部数据集来训练和测试模型性能。还进行了消融研究以验证模型的每个组件。对于数据集Kvasir-SEG和CVC-ClinicDB，所提出的模型的平均骰子得分分别为0.942和0.950，比其他方法更准确。为了展示不同方法的概括，我们处理了两个交叉数据集验证，所提出的模型获得了最高的平均骰子得分。结果表明，所提出的网络具有强大的学习和泛化能力，显着提高分割精度，优于最先进的方法。
结论：在四个不同的公共和一个内部数据集上，所提出的模型比当前的方法产生了更准确的息肉分割。其不同大小的息肉分割能力显示了潜在的临床应用。
