Modeling and manufacturing of personalized cranial implants are important research areas that may decrease the waiting time for patients suffering from cranial damage. The modeling of personalized implants may be partially automated by the use of deep learning-based methods. However, this task suffers from difficulties with generalizability into data from previously unseen distributions that make it difficult to use the research outcomes in real clinical settings. Due to difficulties with acquiring ground-truth annotations, different techniques to improve the heterogeneity of datasets used for training the deep networks have to be considered and introduced. In this work, we present a large-scale study of several augmentation techniques, varying from classical geometric transformations, image registration, variational autoencoders, and generative adversarial networks, to the most recent advances in latent diffusion models. We show that the use of heavy data augmentation significantly increases both the quantitative and qualitative outcomes, resulting in an average Dice Score above 0.94 for the SkullBreak and above 0.96 for the SkullFix datasets. Moreover, we show that the synthetically augmented network successfully reconstructs real clinical defects. The work is a considerable contribution to the field of artificial intelligence in the automatic modeling of personalized cranial implants.
Back to the Roots: Reconstructing Large and Complex Cranial Defects using an Image-based Statistical Shape Model
Institute for Artificial Intelligence in Medicine (IKIM), Essen University Hospital（埃森大学医院医学人工智能研究所（IKIM））；Cancer Research Center Cologne Essen (CCCE), University Medicine Essen（埃森大学医学院科隆-埃森癌症研究中心（CCCE））；German Cancer Consortium (DKTK)（德国癌症研究联盟（DKTK））；Computer Algorithms for Medicine Laboratory (Cafe)（医学计算机算法实验室（Cafe））；Institute of Computer Graphics and Vision (ICG), Graz University of Technology（格拉茨工业大学计算机图形与视觉研究所（ICG））；Department of Neurosurgery, University of Nebraska Medical Center（内布拉斯加大学医学中心神经外科）
Verifier-Guided Synthetic Augmentation for 3D Human Shape Generation
University of South Carolina（南卡罗来纳大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Murdoch University（莫道克大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced Synthetic Data Augmentation and Model Ensemble for brain tumour segmentation
University of Minho（米尼奥大学）；Institute for Artificial Intelligence in Medicine（人工智能医学研究所）；Graz University of Technology（格拉茨工业大学）；University Medicine Essen（埃森大学医学中心）；Cancer Research Center Cologne Essen (CCCE)（科隆-埃森癌症研究中心）；University Hospital Essen（埃森大学医院）；German Cancer Consortium (DKTK)（德国癌症联盟）
Deep Complementary Joint Model for Complex Scene Registration and Few-shot Segmentation on Medical Images
Southeast University（东南大学）；Centre de Recherche en Information Biomédicale Sino-Français (CRIBs)（中法生物医学信息研究中心）；Univ Rennes（雷恩大学）；Inserm（法国国家健康与医学研究院）；LTSI - UMR1099（信号与图像处理实验室）
'A net for everyone': fully personalized and unsupervised neural networks trained with longitudinal data from a single patient
German Cancer Research Center (DKFZ)（德国癌症研究中心）；Heidelberg University（海德堡大学）；University Hospital Essen (AöR)（埃森大学医院）；University Medicine Essen（埃森大学医学院）；German Cancer Consortium (DKTK)（德国癌症联盟）
Deformation-Recovery Diffusion Model (DRDM): Instance Deformation for Image Manipulation and Synthesis
The Kennedy Institute of Rheumatology, University of Oxford, U.K.；Chinese Academy of Medical Sciences Oxford Institute, University of Oxford, U.K.；Big Data Institute, University of Oxford, U.K.；edited MRC Laboratory of Medical Sciences, Imperial College London, U.K.；Department of Computer Science, University of Oxford, Oxford, U.K. [12.5pt] Project page: -5pt
FedMed-ATL: Misaligned Unpaired Brain Image Synthesis via Affine Transform Loss
Southern University of Science and Technology（南方科技大学）；University of Surrey（萨里大学）；Tencent Jarvis Lab（腾讯医疗健康（深圳）有限公司腾讯天衍实验室）；Bielefeld University（比勒费尔德大学）
++nnU-Net: Scaling nnU-Net with Prefix-Based Data Augmentation
Center Algoritmi / LASI, University of Minho, Braga, Portugal（阿尔戈里米中心/拉斯伊大学，明霍大学，布拉加，葡萄牙）；Institute for Artificial Intelligence in Medicine, University Medicine Essen, Essen, Germany（医学人工智能研究所，埃森医学院，埃森，德国）；Institute of Medical Informatics / Dept. of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, Germany（医学信息学研究所/口腔和颅面外科部，亚琛大学医院，德国）；Faculty of Computer Science, University of Duisburg-Essen, Essen, Germany（计算机科学学院，杜伊斯堡-埃森大学，埃森，德国）
Semi-Supervised Deep Metrics for Image Registration
Medical Informatics Laboratory, Queen’s University（女王大学医学信息学实验室）；Radiology Department, Brigham and Women’s Hospital, Harvard Medical School（哈佛医学院布莱根妇女医院放射科）；Graduate School of Frontier Sciences, The University of Tokyo（东京大学前沿科学研究生院）；Department of Electrical and Computer Engineering, University of British Columbia（英属哥伦比亚大学电气与计算机工程系）
Learning deep abdominal CT registration through adaptive loss weighting and synthetic data generation
SINTEF（挪威科学与工业研究基金会）；Norwegian University of Science and Technology (NTNU)（挪威科技大学）；St. Olavs hospital, Trondheim University Hospital（圣奥拉夫医院，特隆赫姆大学医院）；Oslo University Hospital（奥斯陆大学医院）
MICCAI STSR 2025 Challenge: Semi-Supervised Teeth and Pulp Segmentation and CBCT-IOS Registration
Innovation Center for Electronic Design Automation Technology（电子设计自动化技术创新中心）；School of Cyberspace（网络空间学院）；Hangzhou Dianzi University（杭州电子科技大学）；Hangzhou Geriatric Stomatology Hospital（杭州老年牙科医院）；Shenzhen University（深圳大学）；Queen Mary University of London（伦敦女王大学）；Shandong University（山东大学）；School of Automation and Intelligent Sensing（自动化与智能感知学院）；King’s College London（伦敦国王学院）；College of Information Engineering（信息工程学院）；University of Leicester（莱斯特大学）
VINNA for Neonates -- Orientation Independence through Latent Augmentations
German Center for Neurodegenerative Diseases (DZNE)（德国神经退行性疾病中心（DZNE））；A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital（麻省总医院A.A.马蒂诺斯生物医学成像中心）；Harvard Medical School（哈佛医学院）
本文提出 VINNA 框架，通过将四自由度刚体变换模块嵌入网络架构实现分辨率感知的内部增强，在无需图像或标签插值的情况下有效解决新生儿脑 MRI 分割中的头部位置变化和多分辨率问题，并显著优于现有外部增强方法。
Distillation Learning Guided by Image Reconstruction for One-Shot Medical Image Segmentation
School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院）；Peking University Third Hospital（北京大学第三医院）；Beijing Children’s Hospital, Capital Medical University（首都医科大学附属北京儿童医院）；Duke University（杜克大学）
Multiple Sclerosis Lesion Synthesis in MRI using an encoder-decoder U-NET
Research Institute of Computer Vision and Robotics, University of Girona（赫罗纳大学计算机视觉与机器人研究所）；Assiut University（艾斯尤特大学）；Vall d’Hebron University Hospital（瓦尔德希伯伦大学医院）
DeepSSM: A Deep Learning Framework for Statistical Shape Modeling from Raw Images
Scientific Computing and Imaging Institute, University of Utah（犹他大学科学计算与成像研究所）；School of Computing, University of Utah（犹他大学计算学院）；Comprehensive Arrhythmia Research and Management Center, University of Utah（犹他大学综合心律失常研究与管理中心）
Institute of Mathematics（数学研究所）；Image Computing, University of Luebeck, Luebeck, Germany（图像计算，吕贝克大学，吕贝克，德国）；Department of Applied Mathematics（应用数学系）；Theoretical Physics, University of Cambridge, Cambridge, United Kingdom（理论物理，剑桥大学，剑桥，英国）
