Sharing standardized image-derived data in computational pathology using DICOM
本文解决了病理图像衍生数据共享不足的问题，通过将这些数据转换为DICOM标准格式并在NCI IDC平台上公开分享来解决。
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$\texttt{DisMorph}$: learning to disentangle technical distortions from true biological change
In longitudinal MRI, image registration struggles to distinguish geometric distortions caused by technical factors—such as gradient nonlinearity—from genuine anatomical changes, leading to biased morphometric measurements. To address this, this work proposes DisMorph, a novel framework that explicitly decouples technical distortions from biological structural changes during registration. DisMorph employs a generative model trained on synthetic data to predict two separate dense deformation fields corresponding to each component, thereby achieving disentanglement. Domain randomization is further integrated to enhance generalization across imaging protocols. Experiments demonstrate that the method more accurately detects anatomical changes in simulated data, correctly attributes alterations in real image pairs containing only distortion, and effectively identifies both disease-related structural changes and residual distortions in longitudinal Alzheimer’s disease datasets.
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Sharing standardized image-derived data in computational pathology using DICOM
本文解决了病理图像衍生数据共享不足的问题，通过将这些数据转换为DICOM标准格式并在NCI IDC平台上公开分享来解决。
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$\texttt{DisMorph}$: learning to disentangle technical distortions from true biological change
In longitudinal MRI, image registration struggles to distinguish geometric distortions caused by technical factors—such as gradient nonlinearity—from genuine anatomical changes, leading to biased morphometric measurements. To address this, this work proposes DisMorph, a novel framework that explicitly decouples technical distortions from biological structural changes during registration. DisMorph employs a generative model trained on synthetic data to predict two separate dense deformation fields corresponding to each component, thereby achieving disentanglement. Domain randomization is further integrated to enhance generalization across imaging protocols. Experiments demonstrate that the method more accurately detects anatomical changes in simulated data, correctly attributes alterations in real image pairs containing only distortion, and effectively identifies both disease-related structural changes and residual distortions in longitudinal Alzheimer’s disease datasets.
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