TY - JOUR A1 - Ruewe, Marc A1 - Eigenberger, Andreas A1 - Klein, Silvan A1 - von Riedheim, Antonia A1 - Gugg, Christine A1 - Prantl, Lukas A1 - Palm, Christoph A1 - Weiherer, Maximilian A1 - Zeman, Florian A1 - Anker, Alexandra T1 - Precise Monitoring of Returning Sensation in Digital Nerve Lesions by 3-D Imaging: A Proof-of-Concept Study JF - Plastic and Reconstructive Surgery N2 - Digital nerve lesions result in a loss of tactile sensation reflected by an anesthetic area (AA) at the radial or ulnar aspect of the respective digit. Yet, available tools to monitor the recovery of tactile sense have been criticized for their lack of validity. However, the precise quantification of AA dynamics by three-dimensional (3-D) imaging could serve as an accurate surrogate to monitor recovery following digital nerve repair. For validation, AAs were marked on digits of healthy volunteers to simulate the AA of an impaired cutaneous innervation. Three dimensional models were composed from raw images that had been acquired with a 3-D camera (Vectra H2) to precisely quantify relative AA for each digit (3-D models, n= 80). Operator properties varied regarding individual experience in 3-D imaging and image processing. Additionally, the concept was applied in a clinical case study. Images taken by experienced photographers were rated better quality (p< 0.001) and needed less processing time (p= 0.020). Quantification of the relative AA was neither altered significantly by experience levels of the photographer (p= 0.425) nor the image assembler (p= 0.749). The proposed concept allows precise and reliable surface quantification of digits and can be performed consistently without relevant distortion by lack of examiner experience. Routine 3-D imaging of the AA has the great potential to provide visual evidence of various returning states of sensation and to convert sensory nerve recovery into a metric variable with high responsiveness to temporal progress. KW - 3D imaging Y1 - 2023 U6 - https://doi.org/10.1097/PRS.0000000000010456 SN - 1529-4242 VL - 152 IS - 4 SP - 670e EP - 674e PB - Lippincott Williams & Wilkins CY - Philadelphia, Pa. ER - TY - INPR A1 - Weiherer, Maximilian A1 - von Riedheim, Antonia A1 - Brébant, Vanessa A1 - Egger, Bernhard A1 - Palm, Christoph T1 - iRBSM: A Deep Implicit 3D Breast Shape Model N2 - We present the first deep implicit 3D shape model of the female breast, building upon and improving the recently proposed Regensburg Breast Shape Model (RBSM). Compared to its PCA-based predecessor, our model employs implicit neural representations; hence, it can be trained on raw 3D breast scans and eliminates the need for computationally demanding non-rigid registration -- a task that is particularly difficult for feature-less breast shapes. The resulting model, dubbed iRBSM, captures detailed surface geometry including fine structures such as nipples and belly buttons, is highly expressive, and outperforms the RBSM on different surface reconstruction tasks. Finally, leveraging the iRBSM, we present a prototype application to 3D reconstruct breast shapes from just a single image. Model and code publicly available at this https URL. KW - Shape Model KW - Female Breast KW - Regensburg Breast Shape Model Y1 - 2024 U6 - https://doi.org/10.48550/arXiv.2412.13244 ER - TY - CHAP A1 - Weiherer, Maximilian A1 - von Riedheim, Antonia A1 - Brébant, Vanessa A1 - Egger, Bernhard A1 - Palm, Christoph ED - Palm, Christoph ED - Breininger, Katharina ED - Deserno, Thomas M. ED - Handels, Heinz ED - Maier, Andreas ED - Maier-Hein, Klaus H. ED - Tolxdorff, Thomas T1 - iRBSM: A Deep Implicit 3D Breast Shape Model T2 - Bildverarbeitung für die Medizin 2025: Proceedings, German Conference on Medical Image Computing, Regensburg March 09-11, 2025 N2 - We present the first deep implicit 3D shape model of the female breast, building upon and improving the recently proposed Regensburg Breast Shape Model (RBSM). Compared to its PCA-based predecessor, our model employs implicit neural representations; hence, it can be trained on raw 3D breast scans and eliminates the need for computationally demanding non-rigid registration, a task that is particularly difficult for feature-less breast shapes. The resulting model, dubbed iRBSM, captures detailed surface geometry including fine structures such as nipples and belly buttons, is highly expressive, and outperforms the RBSM on different surface reconstruction tasks. Finally, leveraging the iRBSM, we present a prototype application to 3D reconstruct breast shapes from just a single image. Model and code publicly available at https://rbsm.re-mic.de/implicit. Y1 - 2025 U6 - https://doi.org/10.1007/978-3-658-47422-5_11 SP - 38 EP - 43 PB - Springer Vieweg CY - Wiesbaden ER -