胰腺健康与糖尿病等疾病密切相关,准确检测胰腺脂肪含量对疾病的早期诊断和干预具有重要意义.本文提出了一种基于深度学习的胰腺自动分割与脂肪定量方法.首先,使用nnU-Net训练分割模型,实现对m-Dixon序列中胰腺整体的高精度分割,测试集...胰腺健康与糖尿病等疾病密切相关,准确检测胰腺脂肪含量对疾病的早期诊断和干预具有重要意义.本文提出了一种基于深度学习的胰腺自动分割与脂肪定量方法.首先,使用nnU-Net训练分割模型,实现对m-Dixon序列中胰腺整体的高精度分割,测试集DSC系数(Dice Similarity Coefficient,DSC)达0.92.随后,提出一种自动分区与脂肪定量评估方法,实现胰腺头、体、尾的精准划分,并定量分析其体积及脂肪含量.基于256例受试者的研究结果表明,胰腺尾部脂肪含量与2型糖尿病显著相关(p<0.05).进一步利用随机森林分类模型进行糖尿病风险预测,其中基于尾部脂肪含量的分类曲线下面积(Area Under the Curve,AUC)为0.68,而结合多区域脂肪信息构建的组合脂肪含量的分类AUC达0.73.研究结果表明,该方法可为糖尿病的早期诊断提供有效的技术支持.展开更多
Background:Segmentation of abdominal organs in computed tomography(CT)images within clinical oncological workflows is crucial for ensuring effective treatment planning and follow-up.However,manually generated segmenta...Background:Segmentation of abdominal organs in computed tomography(CT)images within clinical oncological workflows is crucial for ensuring effective treatment planning and follow-up.However,manually generated segmentations are time-consuming and labor-intensive in addition to being subject to inter-observer variability.Many deep learning and automated machine learning(AutoML)frameworks have emerged as a solution to this challenge and show promise in clinical workflows.Objective:This study aims to provide a comprehensive evaluation of existing AutoML frameworks(Auto3DSeg,nnU-Net)against a state-of-the-art non-AutoML framework,the Shifted Window U-Net Transformer(SwinUNETR).Methods:Each framework was trained on the same 122 training images,taken from the Abdominal Multi-Organ Segmentation(AMOS)Grand Challenge.Frameworks were compared using dice similarity coefficient(DSC),surface DSC(sDSC),and 95th percentile Hausdorff distances(HD95)on an additional 72 holdout-validation images.The perceived clinical viability of 30 auto-contoured test cases was assessed by three physicians in a blinded evaluation.Results:Comparisons show significantly better performance by AutoML methods:nnU-Net(average DSC:0.924,average sDSC:0.938,average HD95:4.26,median Likert:4.57),Auto3DSeg(average DSC:0.902,average sDSC:0.919,average HD95:8.76,median Likert:4.49),and SwinUNETR(average DSC:0.837,average sDSC:0.844,average HD95:13.93).AutoML frameworks were quantitatively preferred(13/13 organs at risks[OARs]P<0.05 in DSC and sDSC,12/13 OARs P<0.05 in HD95,comparing Auto3DSeg to SwinUNETR,and all OARs P<0.05 in all metrics comparing SwinUNETR to nnU-Net).Qualitatively,nnU-Net was preferred over Auto3DSeg(P=0.0027).Conclusion:The findings suggest that AutoML frameworks offer a significant advantage in the segmentation of abdominal organs,and underscores the potential of AutoML methods to enhance the efficiency of oncological workflows.展开更多
文摘胰腺健康与糖尿病等疾病密切相关,准确检测胰腺脂肪含量对疾病的早期诊断和干预具有重要意义.本文提出了一种基于深度学习的胰腺自动分割与脂肪定量方法.首先,使用nnU-Net训练分割模型,实现对m-Dixon序列中胰腺整体的高精度分割,测试集DSC系数(Dice Similarity Coefficient,DSC)达0.92.随后,提出一种自动分区与脂肪定量评估方法,实现胰腺头、体、尾的精准划分,并定量分析其体积及脂肪含量.基于256例受试者的研究结果表明,胰腺尾部脂肪含量与2型糖尿病显著相关(p<0.05).进一步利用随机森林分类模型进行糖尿病风险预测,其中基于尾部脂肪含量的分类曲线下面积(Area Under the Curve,AUC)为0.68,而结合多区域脂肪信息构建的组合脂肪含量的分类AUC达0.73.研究结果表明,该方法可为糖尿病的早期诊断提供有效的技术支持.
基金funding from the University of Alabama at Birmingham,the National Institutions of Health/National Cancer Institute Award(LRP0000018407)National Center for Advancing Translational Sciences(5KL2TR003097-05).
文摘Background:Segmentation of abdominal organs in computed tomography(CT)images within clinical oncological workflows is crucial for ensuring effective treatment planning and follow-up.However,manually generated segmentations are time-consuming and labor-intensive in addition to being subject to inter-observer variability.Many deep learning and automated machine learning(AutoML)frameworks have emerged as a solution to this challenge and show promise in clinical workflows.Objective:This study aims to provide a comprehensive evaluation of existing AutoML frameworks(Auto3DSeg,nnU-Net)against a state-of-the-art non-AutoML framework,the Shifted Window U-Net Transformer(SwinUNETR).Methods:Each framework was trained on the same 122 training images,taken from the Abdominal Multi-Organ Segmentation(AMOS)Grand Challenge.Frameworks were compared using dice similarity coefficient(DSC),surface DSC(sDSC),and 95th percentile Hausdorff distances(HD95)on an additional 72 holdout-validation images.The perceived clinical viability of 30 auto-contoured test cases was assessed by three physicians in a blinded evaluation.Results:Comparisons show significantly better performance by AutoML methods:nnU-Net(average DSC:0.924,average sDSC:0.938,average HD95:4.26,median Likert:4.57),Auto3DSeg(average DSC:0.902,average sDSC:0.919,average HD95:8.76,median Likert:4.49),and SwinUNETR(average DSC:0.837,average sDSC:0.844,average HD95:13.93).AutoML frameworks were quantitatively preferred(13/13 organs at risks[OARs]P<0.05 in DSC and sDSC,12/13 OARs P<0.05 in HD95,comparing Auto3DSeg to SwinUNETR,and all OARs P<0.05 in all metrics comparing SwinUNETR to nnU-Net).Qualitatively,nnU-Net was preferred over Auto3DSeg(P=0.0027).Conclusion:The findings suggest that AutoML frameworks offer a significant advantage in the segmentation of abdominal organs,and underscores the potential of AutoML methods to enhance the efficiency of oncological workflows.
