Automatic skull defect restoration and cranial implant generation for cranioplasty

颅骨成形术 计算机科学 颅骨 卷积神经网络 人工智能 体素 分割 计算机视觉 任务(项目管理) 医学 解剖 工程类 系统工程
作者
Jianning Li,Gord von Campe,Antonio Pepe,Christina Gsaxner,Enpeng Wang,Xiaojun Chen,Ulrike Zefferer,Martin Tödtling,Marcell Krall,Hannes Deutschmann,Ute Schäfer,Dieter Schmalstieg,Jan Egger
出处
期刊:Medical Image Analysis [Elsevier BV]
卷期号:73: 102171-102171 被引量:33
标识
DOI:10.1016/j.media.2021.102171
摘要

A fast and fully automatic design of 3D printed patient-specific cranial implants is highly desired in cranioplasty - the process to restore a defect on the skull. We formulate skull defect restoration as a 3D volumetric shape completion task, where a partial skull volume is completed automatically. The difference between the completed skull and the partial skull is the restored defect; in other words, the implant that can be used in cranioplasty. To fulfill the task of volumetric shape completion, a fully data-driven approach is proposed. Supervised skull shape learning is performed on a database containing 167 high-resolution healthy skulls. In these skulls, synthetic defects are injected to create training and evaluation data pairs. We propose a patch-based training scheme tailored for dealing with high-resolution and spatially sparse data, which overcomes the disadvantages of conventional patch-based training methods in high-resolution volumetric shape completion tasks. In particular, the conventional patch-based training is applied to images of high resolution and proves to be effective in tasks such as segmentation. However, we demonstrate the limitations of conventional patch-based training for shape completion tasks, where the overall shape distribution of the target has to be learnt, since it cannot be captured efficiently by a sub-volume cropped from the target. Additionally, the standard dense implementation of a convolutional neural network tends to perform poorly on sparse data, such as the skull, which has a low voxel occupancy rate. Our proposed training scheme encourages a convolutional neural network to learn from the high-resolution and spatially sparse data. In our study, we show that our deep learning models, trained on healthy skulls with synthetic defects, can be transferred directly to craniotomy skulls with real defects of greater irregularity, and the results show promise for clinical use. Project page: https://github.com/Jianningli/MIA.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
普鲁卡因发布了新的文献求助10
刚刚
Helios完成签到,获得积分10
1秒前
xueshidaheng完成签到,获得积分0
1秒前
BK_201完成签到,获得积分10
1秒前
风信子完成签到,获得积分10
2秒前
abiorz完成签到,获得积分0
2秒前
窗外是蔚蓝色完成签到,获得积分0
3秒前
nanostu完成签到,获得积分10
5秒前
吐司炸弹完成签到,获得积分10
5秒前
mayfly完成签到,获得积分10
6秒前
Brief完成签到,获得积分10
6秒前
大模型应助科研通管家采纳,获得10
7秒前
小二郎应助科研通管家采纳,获得10
7秒前
7秒前
科研通AI2S应助科研通管家采纳,获得10
7秒前
Amikacin完成签到,获得积分10
7秒前
鹏举瞰冷雨完成签到,获得积分10
7秒前
整齐的惮完成签到 ,获得积分10
8秒前
1122完成签到 ,获得积分10
8秒前
情怀应助Cheung2121采纳,获得30
10秒前
完犊子完成签到,获得积分20
10秒前
神秘玩家完成签到 ,获得积分10
11秒前
heher完成签到 ,获得积分10
15秒前
那些兔儿完成签到 ,获得积分0
18秒前
量子星尘发布了新的文献求助10
19秒前
22秒前
昵称666发布了新的文献求助20
24秒前
29秒前
韭菜盒子发布了新的文献求助10
29秒前
落叶听风笑完成签到,获得积分10
30秒前
西扬完成签到 ,获得积分10
30秒前
Pises完成签到,获得积分10
36秒前
今后应助韭菜盒子采纳,获得10
37秒前
38秒前
大勺完成签到 ,获得积分10
39秒前
科研狗敏敏完成签到,获得积分20
40秒前
牛马完成签到 ,获得积分10
40秒前
43秒前
普鲁卡因完成签到,获得积分10
44秒前
倪塔宝贝完成签到 ,获得积分10
44秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Handbook of Industrial Diamonds.Vol2 1100
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4038112
求助须知:如何正确求助?哪些是违规求助? 3575788
关于积分的说明 11373801
捐赠科研通 3305604
什么是DOI,文献DOI怎么找? 1819255
邀请新用户注册赠送积分活动 892655
科研通“疑难数据库(出版商)”最低求助积分说明 815022