肝肿瘤的早期诊断对提高患者生存率至关重要,而精准的肝肿瘤分割在诊疗过程中具有关键作用。然而,传统的分割方法依赖于医生的手动操作,既耗时耗力,也容易受到医生主观经验的影响。近年来,卷积神经网络和Transformer等技术在肝肿瘤分割...肝肿瘤的早期诊断对提高患者生存率至关重要,而精准的肝肿瘤分割在诊疗过程中具有关键作用。然而,传统的分割方法依赖于医生的手动操作,既耗时耗力,也容易受到医生主观经验的影响。近年来,卷积神经网络和Transformer等技术在肝肿瘤分割上取得了一定进展,但仍面临特征提取不足和收敛速度慢等挑战。具体而言,现有方法通常过于关注肿瘤整体形状、位置等全局信息,而忽视了肿瘤边缘模糊、内部结构复杂等局部细节,这些细节对提高分割精度至关重要。同时,尽管Transformer在捕捉长距离依赖和全局上下文方面具有优势,但未能有效结合肝肿瘤的结构特征,影响了模型的分割效果和效率。为解决这些问题,本文基于3D-UNet提出改进的TGU-Net。首先在跳跃连接中加入了纹理增强模块(Texture Enhancement Module),通过多分支、多尺度3D卷积核选择机制,更好地提取局部特征并捕捉边缘的细微梯度变化,从而提高模型对边缘细节的敏感度和分割精度。其次,在3D-UNet的瓶颈层引入了3D Cross-Shaped Transformer模块(Cross-Shaped Transformer),结合3D Transformer的建模能力与Cross-Shaped自注意力机制,使模型更精准地聚焦于肿瘤区域的语义信息,提高对肿瘤复杂形态的理解能力。为进一步提高模型的训练效率,本文在该模块之前加入3D深度可分离卷积的先验层(Local Encoding Module),通过分离空间和通道的卷积操作,提升了特征提取的效率并加快训练速度。在LiTS2017数据集上的实验验证表明,TGU-Net的IOU和Dice指标分别提升了3.89和2.57个百分点,相较于多种SOTA算法表现优异,证明了其在肝肿瘤分割任务中的优越性。Early diagnosis of liver tumors is critical for improving patient survival rates, and precise liver tumor segmentation plays a key role in treatment planning. However, traditional segmentation methods rely on manual operations by clinicians, which are time-consuming, labor-intensive, and often influenced by subjective experience. Recently, technologies like convolutional neural networks (CNNs) and Transformers have achieved progress in liver tumor segmentation, yet challenges remain in feature extraction and model convergence speed. Specifically, existing methods often overemphasize global features, such as the overall shape, location, and size of the tumor, while overlooking local details, including blurred tumor edges and complex internal structures, which are essential for improving segmentation accuracy. Although Transformers excel at capturing long-range dependencies and global context, they have yet to effectively incorporate the structural characteristics of liver tumors, impacting segmentation performance and model efficiency. To address these issues, this paper proposes an enhanced TGU-Net model based on the 3D-UNet architecture. First, a Texture Enhancement Module is introduced into the skip connections, employing a multi-branch, multi-scale 3D convolutional kernel selection mechanism. This module better captures local features and fine gradient changes around tumor edges, thereby enhancing the model’s sensitivity to edge details and improving segmentation accuracy. Next, a 3D Cross-Shaped Transformer module is incorporated in the bottleneck layer of 3D-UNet. By combining the 3D Transformer’s modeling capability with Cross-Shaped self-attention, the model achieves more precise focus on the semantic information of tumor regions, enhancing its ability to understand complex tumor morphologies. To further improve training efficiency, a Local Encoding Module using 3D depthwise separable convolutions is added before this module, separating spatial and channel convolutions to accelerate training and improve feature extraction efficiency. Experimental validation on the LiTS2017 dataset demonstrates that TGU-Net improves IOU and Dice scores by 3.89 and 2.57 percentage points, respectively, outperforming multiple state-of-the-art algorithms and underscoring its superiority in liver tumor segmentation tasks.展开更多
目的本研究旨在探寻一类性能优异的血管增强算法,并结合阈值水平集分割算法进行肝脏血管系统的三维自动分割。方法首先对原始三维增强CT数据进行S型非线性灰度映射;随后对不同的血管增强算法进行对比分析;最后使用阈值水平集分割算法分...目的本研究旨在探寻一类性能优异的血管增强算法,并结合阈值水平集分割算法进行肝脏血管系统的三维自动分割。方法首先对原始三维增强CT数据进行S型非线性灰度映射;随后对不同的血管增强算法进行对比分析;最后使用阈值水平集分割算法分割出肝血管系统。选用3Dircadb公开数据集中的20例腹部增强CT数据定量评估了两类经典的血管增强算法,包括血管特征提取算法及扩散滤波算法。结果血管特征提取算法运行效率平均优于扩散滤波算法。血管特征提取算法结果的对比度平均高于扩散滤波算法2 d B以上,导致扩散滤波算法后续的计算复杂度高,准确性降低。阈值水平集分割算法的结果与区域生长算法、形态检测水平集算法和测地线活动轮廓水平集算法相比,准确性达77%以上,高于其余分割算法。结论血管特征提取算法与扩散滤波算法相比,更适合依赖灰度值的血管分割。阈值水平集算法能缓解单纯依赖阈值或依赖血管边界的血管欠分割问题,结合血管增强算法后能更准确的分割出肝脏血管。展开更多
晚期肝癌为肝胆外科治疗的难点,联合肝脏分割和门静脉结扎的分阶段肝切除术(associating liver partition and portal vein ligation for staged hepatectomy,ALPPS)是近年来开展的一种新型晚期肝癌根治术式,本文就ALPPS应用现状及进展...晚期肝癌为肝胆外科治疗的难点,联合肝脏分割和门静脉结扎的分阶段肝切除术(associating liver partition and portal vein ligation for staged hepatectomy,ALPPS)是近年来开展的一种新型晚期肝癌根治术式,本文就ALPPS应用现状及进展进行综述。展开更多
介绍了马尔可夫随机场(markov random field,MRF)的基本理论,以及基于MRF的图像分割模型及其求解过程。利用MRF分割方法对肝脏CT图片进行了分割,实验结果表明:该方法能够有效对肝脏实质进行分割,在一些模糊区域有更好的分割效果,可用于C...介绍了马尔可夫随机场(markov random field,MRF)的基本理论,以及基于MRF的图像分割模型及其求解过程。利用MRF分割方法对肝脏CT图片进行了分割,实验结果表明:该方法能够有效对肝脏实质进行分割,在一些模糊区域有更好的分割效果,可用于CT图像序列中的肝实质自动分割。展开更多
文摘肝肿瘤的早期诊断对提高患者生存率至关重要,而精准的肝肿瘤分割在诊疗过程中具有关键作用。然而,传统的分割方法依赖于医生的手动操作,既耗时耗力,也容易受到医生主观经验的影响。近年来,卷积神经网络和Transformer等技术在肝肿瘤分割上取得了一定进展,但仍面临特征提取不足和收敛速度慢等挑战。具体而言,现有方法通常过于关注肿瘤整体形状、位置等全局信息,而忽视了肿瘤边缘模糊、内部结构复杂等局部细节,这些细节对提高分割精度至关重要。同时,尽管Transformer在捕捉长距离依赖和全局上下文方面具有优势,但未能有效结合肝肿瘤的结构特征,影响了模型的分割效果和效率。为解决这些问题,本文基于3D-UNet提出改进的TGU-Net。首先在跳跃连接中加入了纹理增强模块(Texture Enhancement Module),通过多分支、多尺度3D卷积核选择机制,更好地提取局部特征并捕捉边缘的细微梯度变化,从而提高模型对边缘细节的敏感度和分割精度。其次,在3D-UNet的瓶颈层引入了3D Cross-Shaped Transformer模块(Cross-Shaped Transformer),结合3D Transformer的建模能力与Cross-Shaped自注意力机制,使模型更精准地聚焦于肿瘤区域的语义信息,提高对肿瘤复杂形态的理解能力。为进一步提高模型的训练效率,本文在该模块之前加入3D深度可分离卷积的先验层(Local Encoding Module),通过分离空间和通道的卷积操作,提升了特征提取的效率并加快训练速度。在LiTS2017数据集上的实验验证表明,TGU-Net的IOU和Dice指标分别提升了3.89和2.57个百分点,相较于多种SOTA算法表现优异,证明了其在肝肿瘤分割任务中的优越性。Early diagnosis of liver tumors is critical for improving patient survival rates, and precise liver tumor segmentation plays a key role in treatment planning. However, traditional segmentation methods rely on manual operations by clinicians, which are time-consuming, labor-intensive, and often influenced by subjective experience. Recently, technologies like convolutional neural networks (CNNs) and Transformers have achieved progress in liver tumor segmentation, yet challenges remain in feature extraction and model convergence speed. Specifically, existing methods often overemphasize global features, such as the overall shape, location, and size of the tumor, while overlooking local details, including blurred tumor edges and complex internal structures, which are essential for improving segmentation accuracy. Although Transformers excel at capturing long-range dependencies and global context, they have yet to effectively incorporate the structural characteristics of liver tumors, impacting segmentation performance and model efficiency. To address these issues, this paper proposes an enhanced TGU-Net model based on the 3D-UNet architecture. First, a Texture Enhancement Module is introduced into the skip connections, employing a multi-branch, multi-scale 3D convolutional kernel selection mechanism. This module better captures local features and fine gradient changes around tumor edges, thereby enhancing the model’s sensitivity to edge details and improving segmentation accuracy. Next, a 3D Cross-Shaped Transformer module is incorporated in the bottleneck layer of 3D-UNet. By combining the 3D Transformer’s modeling capability with Cross-Shaped self-attention, the model achieves more precise focus on the semantic information of tumor regions, enhancing its ability to understand complex tumor morphologies. To further improve training efficiency, a Local Encoding Module using 3D depthwise separable convolutions is added before this module, separating spatial and channel convolutions to accelerate training and improve feature extraction efficiency. Experimental validation on the LiTS2017 dataset demonstrates that TGU-Net improves IOU and Dice scores by 3.89 and 2.57 percentage points, respectively, outperforming multiple state-of-the-art algorithms and underscoring its superiority in liver tumor segmentation tasks.
文摘目的本研究旨在探寻一类性能优异的血管增强算法,并结合阈值水平集分割算法进行肝脏血管系统的三维自动分割。方法首先对原始三维增强CT数据进行S型非线性灰度映射;随后对不同的血管增强算法进行对比分析;最后使用阈值水平集分割算法分割出肝血管系统。选用3Dircadb公开数据集中的20例腹部增强CT数据定量评估了两类经典的血管增强算法,包括血管特征提取算法及扩散滤波算法。结果血管特征提取算法运行效率平均优于扩散滤波算法。血管特征提取算法结果的对比度平均高于扩散滤波算法2 d B以上,导致扩散滤波算法后续的计算复杂度高,准确性降低。阈值水平集分割算法的结果与区域生长算法、形态检测水平集算法和测地线活动轮廓水平集算法相比,准确性达77%以上,高于其余分割算法。结论血管特征提取算法与扩散滤波算法相比,更适合依赖灰度值的血管分割。阈值水平集算法能缓解单纯依赖阈值或依赖血管边界的血管欠分割问题,结合血管增强算法后能更准确的分割出肝脏血管。
文摘晚期肝癌为肝胆外科治疗的难点,联合肝脏分割和门静脉结扎的分阶段肝切除术(associating liver partition and portal vein ligation for staged hepatectomy,ALPPS)是近年来开展的一种新型晚期肝癌根治术式,本文就ALPPS应用现状及进展进行综述。
