PDF(Pubmed)
Abstract：
UNASSIGNED: Soft tissue sarcomas, similar in incidence to cervical and esophageal cancers, arise from various soft tissues like smooth muscle, fat, and fibrous tissue. Effective segmentation of sarcomas in imaging is crucial for accurate diagnosis.
UNASSIGNED: This study collected multi-modal MRI images from 45 patients with thigh soft tissue sarcoma, totaling 8,640 images. These images were annotated by clinicians to delineate the sarcoma regions, creating a comprehensive dataset. We developed a novel segmentation model based on the UNet framework, enhanced with residual networks and attention mechanisms for improved modality-specific information extraction. Additionally, self-supervised learning strategies were employed to optimize feature extraction capabilities of the encoders.
UNASSIGNED: The new model demonstrated superior segmentation performance when using multi-modal MRI images compared to single-modal inputs. The effectiveness of the model in utilizing the created dataset was validated through various experimental setups, confirming the enhanced ability to characterize tumor regions across different modalities.
UNASSIGNED: The integration of multi-modal MRI images and advanced machine learning techniques in our model significantly improves the segmentation of soft tissue sarcomas in thigh imaging. This advancement aids clinicians in better diagnosing and understanding the patient\'s condition, leveraging the strengths of different imaging modalities. Further studies could explore the application of these techniques to other types of soft tissue sarcomas and additional anatomical sites.
UNASSIGNED: This study collected multi-modal MRI images from 45 patients with thigh soft tissue sarcoma, totaling 8,640 images. These images were annotated by clinicians to delineate the sarcoma regions, creating a comprehensive dataset. We developed a novel segmentation model based on the UNet framework, enhanced with residual networks and attention mechanisms for improved modality-specific information extraction. Additionally, self-supervised learning strategies were employed to optimize feature extraction capabilities of the encoders.
UNASSIGNED: The new model demonstrated superior segmentation performance when using multi-modal MRI images compared to single-modal inputs. The effectiveness of the model in utilizing the created dataset was validated through various experimental setups, confirming the enhanced ability to characterize tumor regions across different modalities.
UNASSIGNED: The integration of multi-modal MRI images and advanced machine learning techniques in our model significantly improves the segmentation of soft tissue sarcomas in thigh imaging. This advancement aids clinicians in better diagnosing and understanding the patient\'s condition, leveraging the strengths of different imaging modalities. Further studies could explore the application of these techniques to other types of soft tissue sarcomas and additional anatomical sites.
摘要：
■软组织肉瘤，与宫颈癌和食道癌的发病率相似，来自各种软组织，如平滑肌，脂肪，和纤维组织。成像中肉瘤的有效分割对于准确诊断至关重要。
■本研究收集了45例大腿软组织肉瘤患者的多模态MRI图像，总计8,640张图像。这些图像由临床医生注释以描绘肉瘤区域，创建一个全面的数据集。我们基于UNet框架开发了一种新颖的细分模型，用残差网络和注意力机制增强，以改进特定于模态的信息提取。此外,采用自监督学习策略来优化编码器的特征提取能力。
■与单模态输入相比，新模型在使用多模态MRI图像时表现出优越的分割性能。通过各种实验设置验证了模型利用创建的数据集的有效性，确认增强的能力，以表征肿瘤区域在不同的模式。
■多模态MRI图像和先进的机器学习技术在我们的模型中的集成显着改善了大腿成像中软组织肉瘤的分割。这一进步有助于临床医生更好地诊断和了解患者的病情，利用不同成像方式的优势。进一步的研究可以探索这些技术在其他类型的软组织肉瘤和其他解剖部位的应用。
■本研究收集了45例大腿软组织肉瘤患者的多模态MRI图像，总计8,640张图像。这些图像由临床医生注释以描绘肉瘤区域，创建一个全面的数据集。我们基于UNet框架开发了一种新颖的细分模型，用残差网络和注意力机制增强，以改进特定于模态的信息提取。此外,采用自监督学习策略来优化编码器的特征提取能力。
■与单模态输入相比，新模型在使用多模态MRI图像时表现出优越的分割性能。通过各种实验设置验证了模型利用创建的数据集的有效性，确认增强的能力，以表征肿瘤区域在不同的模式。
■多模态MRI图像和先进的机器学习技术在我们的模型中的集成显着改善了大腿成像中软组织肉瘤的分割。这一进步有助于临床医生更好地诊断和了解患者的病情，利用不同成像方式的优势。进一步的研究可以探索这些技术在其他类型的软组织肉瘤和其他解剖部位的应用。
