Extending TotalSegmentator: Predicting Patient and Acquisition Characteristics from CT and MR Images
研究开发并评估了一种快速开源模型，通过CT和MR图像直接预测患者及采集特征，使用3D ResNet-10集成方法训练模型。
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Report Supervision
为解决肿瘤分割模型因标注数据稀缺的问题，提出Report Supervision方法，利用放射报告直接监督并改进肿瘤分割模型，提高AI性能。
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Generalizing imaging biomarker repeatability studies using Bayesian inference: Applications in detecting heterogeneous treatment response in whole-body diffusion-weighted MRI of metastatic prostate cancer
Current imaging biomarkers lack a robust, quantitative framework for assessing heterogeneous treatment responses across multiple metastatic lesions in prostate cancer. Method: We propose the first general Bayesian repeatability assessment framework specifically designed for imaging biomarkers—overcoming limitations of traditional normality assumptions by enabling posterior inference for heterogeneous parameters under complex distributions (e.g., Dirichlet-Multinomial), accommodating multimodal imaging (e.g., whole-body diffusion-weighted MRI) and non-Gaussian biomarkers. Efficient Bayesian inference is implemented via Hamiltonian Monte Carlo, integrated with whole-body DWI volumetric analysis and validated on an mCRPC clinical cohort. Results: In two independent studies, the framework quantitatively identified significantly differential treatment responses in ~70% of metastatic lesions, demonstrating clinical validity and substantially improving the accuracy, robustness, and interpretability of heterogeneity quantification.
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Signal-based AI-driven software solution for automated quantification of metastatic bone disease and treatment response assessment using Whole-Body Diffusion-Weighted MRI (WB-DWI) biomarkers in Advanced Prostate Cancer
This study addresses the challenge of quantitative assessment of bone metastatic burden and treatment response in advanced prostate cancer. Methodologically, we propose a fully automated AI framework based on whole-body diffusion-weighted MRI (WB-DWI): (1) a weakly supervised Residual U-Net generates skeletal probability maps to guide lesion detection; (2) a WB-DWI intensity statistical normalization strategy is introduced; and (3) a lightweight CNN enables end-to-end lesion segmentation, followed by registration with gADC maps to extract tumor diffusion volume (TDV) and median gADC—key quantitative biomarkers. Our key contribution is enabling bone metastasis quantification without per-lesion annotation. Validation demonstrates skeletal segmentation Dice scores of 0.6 (pelvis/spine), coefficient of variation (CV) of 4.6% for log-TDV and 3.6% for median gADC, and treatment response classification accuracy of 80.5%, sensitivity of 84.3%, and specificity of 85.7%. Processing time per case is 90 seconds.
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A weakly-supervised deep learning model for fast localisation and delineation of the skeleton, internal organs, and spinal canal on Whole-Body Diffusion-Weighted MRI (WB-DWI)
Clinical whole-body diffusion-weighted imaging (WB-DWI) requires precise anatomical segmentation for accurate ADC quantification and tumor volume (TDV) measurement. However, manual delineation of the entire skeleton, visceral organs (liver, spleen, kidneys, bladder), and spinal canal is prohibitively time-consuming and clinically infeasible. To address this, we propose the first weakly supervised segmentation framework tailored for WB-DWI: a soft-label–guided 3D residual U-Net enabling simultaneous probabilistic segmentation of multiple structures without voxel-level annotations. Leveraging multi-center data, patch-based training, and probabilistic output maps, our method achieves clinical efficiency—25 seconds per case (12× faster than conventional approaches)—while attaining mean Dice scores of 0.66 (skeleton), 0.80 (organs), and 0.85 (spinal canal), with surface distances <3 mm. Quantitative errors in ADC and volume measurements are <10% and <4%, respectively. Radiologists rated the results as “good to excellent.”
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Extending TotalSegmentator: Predicting Patient and Acquisition Characteristics from CT and MR Images
研究开发并评估了一种快速开源模型，通过CT和MR图像直接预测患者及采集特征，使用3D ResNet-10集成方法训练模型。
0 citationsRead paper
Report Supervision
为解决肿瘤分割模型因标注数据稀缺的问题，提出Report Supervision方法，利用放射报告直接监督并改进肿瘤分割模型，提高AI性能。
0 citationsRead paper
Generalizing imaging biomarker repeatability studies using Bayesian inference: Applications in detecting heterogeneous treatment response in whole-body diffusion-weighted MRI of metastatic prostate cancer
Current imaging biomarkers lack a robust, quantitative framework for assessing heterogeneous treatment responses across multiple metastatic lesions in prostate cancer. Method: We propose the first general Bayesian repeatability assessment framework specifically designed for imaging biomarkers—overcoming limitations of traditional normality assumptions by enabling posterior inference for heterogeneous parameters under complex distributions (e.g., Dirichlet-Multinomial), accommodating multimodal imaging (e.g., whole-body diffusion-weighted MRI) and non-Gaussian biomarkers. Efficient Bayesian inference is implemented via Hamiltonian Monte Carlo, integrated with whole-body DWI volumetric analysis and validated on an mCRPC clinical cohort. Results: In two independent studies, the framework quantitatively identified significantly differential treatment responses in ~70% of metastatic lesions, demonstrating clinical validity and substantially improving the accuracy, robustness, and interpretability of heterogeneity quantification.
0 citationsRead paper
Signal-based AI-driven software solution for automated quantification of metastatic bone disease and treatment response assessment using Whole-Body Diffusion-Weighted MRI (WB-DWI) biomarkers in Advanced Prostate Cancer
This study addresses the challenge of quantitative assessment of bone metastatic burden and treatment response in advanced prostate cancer. Methodologically, we propose a fully automated AI framework based on whole-body diffusion-weighted MRI (WB-DWI): (1) a weakly supervised Residual U-Net generates skeletal probability maps to guide lesion detection; (2) a WB-DWI intensity statistical normalization strategy is introduced; and (3) a lightweight CNN enables end-to-end lesion segmentation, followed by registration with gADC maps to extract tumor diffusion volume (TDV) and median gADC—key quantitative biomarkers. Our key contribution is enabling bone metastasis quantification without per-lesion annotation. Validation demonstrates skeletal segmentation Dice scores of 0.6 (pelvis/spine), coefficient of variation (CV) of 4.6% for log-TDV and 3.6% for median gADC, and treatment response classification accuracy of 80.5%, sensitivity of 84.3%, and specificity of 85.7%. Processing time per case is 90 seconds.
0 citationsRead paper
A weakly-supervised deep learning model for fast localisation and delineation of the skeleton, internal organs, and spinal canal on Whole-Body Diffusion-Weighted MRI (WB-DWI)
Clinical whole-body diffusion-weighted imaging (WB-DWI) requires precise anatomical segmentation for accurate ADC quantification and tumor volume (TDV) measurement. However, manual delineation of the entire skeleton, visceral organs (liver, spleen, kidneys, bladder), and spinal canal is prohibitively time-consuming and clinically infeasible. To address this, we propose the first weakly supervised segmentation framework tailored for WB-DWI: a soft-label–guided 3D residual U-Net enabling simultaneous probabilistic segmentation of multiple structures without voxel-level annotations. Leveraging multi-center data, patch-based training, and probabilistic output maps, our method achieves clinical efficiency—25 seconds per case (12× faster than conventional approaches)—while attaining mean Dice scores of 0.66 (skeleton), 0.80 (organs), and 0.85 (spinal canal), with surface distances <3 mm. Quantitative errors in ADC and volume measurements are <10% and <4%, respectively. Radiologists rated the results as “good to excellent.”
0 citationsRead paper