Publications

Alzheimer’s disease in vivo imaging biomarker

Li, Y., Xie, L., Khandelwal, P., Wisse, L.E., Brown, C.A., Prabhakaran, K., Tisdall, M.D., Mechanic‐Hamilton, D., Detre, J.A., Das, S.R. et al., 2025. Automatic Segmentation of Medial Temporal Lobe Subregions in Multi‐Scanner, Multi‐Modality Magnetic Resonance Imaging of Variable Quality. Hippocampus, 35(6), p.e70036.
Li, Y., Khandelwal, P., Jena, R., Xie, L., Duong, M., Denning, A.E., Brown, C.A., Wisse, L.E., Das, S.R., Wolk, D.A. et al., 2025. Achieving detailed medial temporal lobe segmentation with upsampled isotropic training from implicit neural representation. arXiv preprint arXiv:2508.17171.
Li, Y., Khandelwal, P., Xie, L., Wisse, L.E.M., Denning, A.E., Brown, C.A., McGrew, E., Lim, S.A., Sadeghpour, N., Ravikumar, S. et al., 2025. Imaging Biomarkers for Neurodegenerative Diseases from Detailed Segmentation of Medial Temporal Lobe Subregions on in vivo Brain MRI Using Upsampling Strategy Guided by High-resolution ex vivo MRI. arXiv preprint arXiv:2504.18442 (accepted by MICCAI Workshop ML-CDS 2025).
Li, Y., Khandelwal, P., Xie, L., Brown, C.D., Lyu, X., Denning, A.E., Dong, M., Das, S.R., Wolk, D.A. and Yushkevich, P.A., 2024. Robust multi‐modality segmentation of medial temporal lobe subregions using both 3‐tesla and 7‐tesla magnetic resonance imaging. Alzheimer’s & Dementia, 20, p.e094095 (AAIC Abstract).
Yushkevich, P.A., Ittyerah, R., Li, Y., Denning, A.E., Sadeghpour, N., Lim, S., McGrew, E., Xie, L., DeFlores, R., Brown, C.A. et al., 2024. Morphometry of medial temporal lobe subregions using high‐resolution T2‐weighted MRI in ADNI3: Why, how, and what’s next?. Alzheimer’s & Dementia, 20(11), pp.8113-8128 (AAIC Abstract).
Xie, L., Das, S.R., Li, Y., Wisse, L.E., McGrew, E., Lyu, X., DiCalogero, M., Shah, U., Ilesanmi, A., Denning, A.E. et al., 2025. A multi‐cohort study of longitudinal and cross‐sectional Alzheimer’s disease biomarkers in cognitively unimpaired older adults. Alzheimer’s & Dementia, 21(2), p.e14492 (AAIC Abstract).
Denning, A.E., Lim, S.A., Li, Y., Sadeghpour, N., Ravikumar, S., Ittyerah, R., Chung, E., Bedard, M., Prabhakaran, K., Trotman, W. et al., 2024. Mapping Anatomical Boundaries from Histology to Antemortem MRI to Inform Automatic Segmentation of Medial Temporal Lobe Subregions. Alzheimer’s & Dementia, 20, p.e091831 (AAIC Abstract).

Breast lesion detection in DBT

Li, Y., He, Z., Pan, J., Zeng, W., Liu, J., Zeng, Z., Xu, W., Xu, Z., Wang, S., Wen, C. et al., 2023. Atypical architectural distortion detection in digital breast tomosynthesis: a computer-aided detection model with adaptive receptive field. Physics in Medicine & Biology, 68(4), p.045013.
Li, Y., He, Z., Ma, X., Zeng, W., Liu, J., Xu, W., Xu, Z., Wang, S., Wen, C., Zeng, H. et al., 2022. Architectural distortion detection based on superior–inferior directional context and anatomic prior knowledge in digital breast tomosynthesis. Medical Physics, 49(6), pp.3749-3768.
Li, Y., He, Z., Lu, Y., Ma, X., Guo, Y., Xie, Z., Qin, G., Xu, W., Xu, Z., Chen, W. et al., 2021. Deep learning of mammary gland distribution for architectural distortion detection in digital breast tomosynthesis. Physics in Medicine & Biology, 66(3), p.035028.
Li, Y., He, Z., Ma, X., Zeng, W., Liu, J., Xu, W., Xu, Z., Wang, S., Wen, C., Zeng, H. et al., 2022. Computer-aided detection for architectural distortion: a comparison of digital breast tomosynthesis and digital mammography. In Medical Imaging 2022: Computer-Aided Diagnosis (Vol. 12033, pp. 245-252). SPIE.
Li, Y., He, Z., Ma, X., Xu, W., Wen, C., Zeng, H., Zeng, W., Wu, Z., Qin, G., Chen, W. et al., 2021. Architectural distortion detection in digital breast tomosynthesis with adaptive receptive field and adaptive convolution kernel shape. In Medical Imaging 2021: Computer-Aided Diagnosis (Vol. 11597, pp. 592-599). SPIE.
Li, Y., Xie, Z., He, Z., Ma, X., Guo, Y., Chen, W. and Lu, Y., 2020. Architectural distortion detection approach guided by mammary gland spatial pattern in digital breast tomosynthesis. In Medical Imaging 2020: Computer-Aided Diagnosis (Vol. 11314, pp. 285-290). SPIE.
Pan, J., He, Z., Li, Y., Zeng, W., Guo, Y., Jia, L., Jiang, H., Chen, W. and Lu, Y., 2023. Atypical architectural distortion detection in digital breast tomosynthesis: a multi-view computer-aided detection model with ipsilateral learning. Physics in Medicine & Biology, 68(23), p.235006.
Ma, X., He, Z., Li, Y., Zeng, W., Pan, J., Liu, J., Xu, W., Xu, Z., Wang, S., Wen, C. et al., 2023. Multi-view based computer-aided model with anatomical position prior for architectural distortion detection in digital breast tomosynthesis. In Medical Imaging 2023: Computer-Aided Diagnosis (Vol. 12465, pp. 676-681). SPIE.