TY - GEN
T1 - MedEdit
T2 - 9th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2024, held in conjunction with the 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024
AU - Alaya, Malek Ben
AU - Lang, Daniel M.
AU - Wiestler, Benedikt
AU - Schnabel, Julia A.
AU - Bercea, Cosmin I.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - Denoising diffusion probabilistic models enable high-fidelity image synthesis and editing. In biomedicine, these models facilitate counterfactual image editing, producing pairs of images where one is edited to simulate hypothetical conditions. For example, they can model the progression of specific diseases, such as stroke lesions. However, current image editing techniques often fail to generate realistic biomedical counterfactuals, either by inadequately modeling indirect pathological effects like brain atrophy or by excessively altering the scan, which disrupts correspondence to the original images. Here, we propose MedEdit, a conditional diffusion model for medical image editing. MedEdit induces pathology in specific areas while balancing the modeling of disease effects and preserving the original scan’s integrity. We evaluated MedEdit on the Atlas v2.0 stroke dataset using Frechet Inception Distance and Dice scores, outperforming state-of-the-art diffusion-based methods such as Palette (by 45%) and SDEdit (by 61%). Additionally, clinical evaluations by a board-certified neuroradiologist confirmed that MedEdit generated realistic stroke scans indistinguishable from real ones. We believe this work will enable counterfactual image editing research to further advance the development of realistic and clinically useful imaging tools.
AB - Denoising diffusion probabilistic models enable high-fidelity image synthesis and editing. In biomedicine, these models facilitate counterfactual image editing, producing pairs of images where one is edited to simulate hypothetical conditions. For example, they can model the progression of specific diseases, such as stroke lesions. However, current image editing techniques often fail to generate realistic biomedical counterfactuals, either by inadequately modeling indirect pathological effects like brain atrophy or by excessively altering the scan, which disrupts correspondence to the original images. Here, we propose MedEdit, a conditional diffusion model for medical image editing. MedEdit induces pathology in specific areas while balancing the modeling of disease effects and preserving the original scan’s integrity. We evaluated MedEdit on the Atlas v2.0 stroke dataset using Frechet Inception Distance and Dice scores, outperforming state-of-the-art diffusion-based methods such as Palette (by 45%) and SDEdit (by 61%). Additionally, clinical evaluations by a board-certified neuroradiologist confirmed that MedEdit generated realistic stroke scans indistinguishable from real ones. We believe this work will enable counterfactual image editing research to further advance the development of realistic and clinically useful imaging tools.
KW - Biomedical imaging
KW - Conditional Multimodal Learning
UR - http://www.scopus.com/inward/record.url?scp=85207039680&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-73281-2_16
DO - 10.1007/978-3-031-73281-2_16
M3 - Conference contribution
AN - SCOPUS:85207039680
SN - 9783031732805
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 167
EP - 176
BT - Simulation and Synthesis in Medical Imaging - 9th International Workshop, SASHIMI 2024, Held in Conjunction with MICCAI 2024, Proceedings
A2 - Fernandez, Virginia
A2 - Wolterink, Jelmer M.
A2 - Wiesner, David
A2 - Remedios, Samuel
A2 - Zuo, Lianrui
A2 - Casamitjana, Adrià
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 10 October 2024 through 10 October 2024
ER -