TY - GEN
T1 - RANDose
T2 - 28th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025
AU - Chowdary, G. Jignesh
AU - Zhang, Tiezhi
AU - Qian, Xin
AU - Yin, Zhaozheng
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2026
Y1 - 2026
N2 - External Radiation Therapy (ERT) is a key treatment in oncology, aiming to deliver high radiation doses to the Planned Target Volume (PTV) while minimizing exposure to surrounding healthy tissues and Organs At Risk (OARs). However, the proximity of PTVs to OARs, the presence of multiple OARs, and the time-consuming nature of manual subjective dose planning present significant challenges. While recent advancements in Deep Learning (DL) have led to various DL-based methods for dose prediction, it is still challenging to effectively capture multi-scale features and propagate essential information to related regions. In this work, we propose the Region-aware Attention Net (RANDose), which addresses these issues by integrating Multi-Scale Channel Spatial Attention (MSCSA), PTV Integration (PI), and Attention Fusion (AF) modules. Additionally, we introduce a Region-Aware Loss function to ensure accurate dose distribution within the PTV while minimizing radiation exposure to OARs. Experiments on the OpenKBP dataset demonstrate that RANDose outperforms existing models in both Dose Score and Dose Volume Histogram (DVH) Score, highlighting its superior performance.
AB - External Radiation Therapy (ERT) is a key treatment in oncology, aiming to deliver high radiation doses to the Planned Target Volume (PTV) while minimizing exposure to surrounding healthy tissues and Organs At Risk (OARs). However, the proximity of PTVs to OARs, the presence of multiple OARs, and the time-consuming nature of manual subjective dose planning present significant challenges. While recent advancements in Deep Learning (DL) have led to various DL-based methods for dose prediction, it is still challenging to effectively capture multi-scale features and propagate essential information to related regions. In this work, we propose the Region-aware Attention Net (RANDose), which addresses these issues by integrating Multi-Scale Channel Spatial Attention (MSCSA), PTV Integration (PI), and Attention Fusion (AF) modules. Additionally, we introduce a Region-Aware Loss function to ensure accurate dose distribution within the PTV while minimizing radiation exposure to OARs. Experiments on the OpenKBP dataset demonstrate that RANDose outperforms existing models in both Dose Score and Dose Volume Histogram (DVH) Score, highlighting its superior performance.
KW - Attention
KW - Deep learning
KW - Multi-Scale Feature fusion
KW - Radiation Dose Prediction
UR - https://www.scopus.com/pages/publications/105017953223
U2 - 10.1007/978-3-032-05182-0_51
DO - 10.1007/978-3-032-05182-0_51
M3 - Conference contribution
AN - SCOPUS:105017953223
SN - 9783032051813
T3 - Lecture Notes in Computer Science
SP - 523
EP - 532
BT - Medical Image Computing and Computer Assisted Intervention, MICCAI 2025 - 28th International Conference, Proceedings
A2 - Gee, James C.
A2 - Hong, Jaesung
A2 - Sudre, Carole H.
A2 - Golland, Polina
A2 - Alexander, Daniel C.
A2 - Iglesias, Juan Eugenio
A2 - Venkataraman, Archana
A2 - Kim, Jong Hyo
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 23 September 2025 through 27 September 2025
ER -