已入深夜,您辛苦了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!祝你早点完成任务,早点休息,好梦!

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
小蘑菇应助dapis采纳,获得10
1秒前
SciGPT应助Steven采纳,获得10
1秒前
研友_LNM558发布了新的文献求助50
4秒前
4秒前
4秒前
桐桐应助orange9采纳,获得10
4秒前
缥缈的松鼠完成签到 ,获得积分10
5秒前
5秒前
5秒前
8秒前
大面包发布了新的文献求助10
9秒前
恋雅颖月应助方睿智采纳,获得10
15秒前
Sophia发布了新的文献求助10
15秒前
16秒前
zgl完成签到,获得积分10
17秒前
易寒完成签到,获得积分10
18秒前
19秒前
隐形曼青应助SunHY采纳,获得10
19秒前
eueurhj发布了新的文献求助60
20秒前
sean完成签到,获得积分10
20秒前
24秒前
24秒前
25秒前
25秒前
26秒前
Able完成签到,获得积分10
27秒前
27秒前
Coinish丶Fuhua完成签到,获得积分10
28秒前
28秒前
负责怀莲完成签到,获得积分10
28秒前
29秒前
fu发布了新的文献求助10
30秒前
SunHY发布了新的文献求助10
30秒前
大面包发布了新的文献求助10
32秒前
tt发布了新的文献求助10
32秒前
负责怀莲发布了新的文献求助10
33秒前
体贴绝音发布了新的文献求助10
33秒前
SHAO应助SunHY采纳,获得30
35秒前
36秒前
orixero应助杨行肖采纳,获得10
41秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
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 3989857
求助须知:如何正确求助?哪些是违规求助? 3531994
关于积分的说明 11255679
捐赠科研通 3270758
什么是DOI,文献DOI怎么找? 1805053
邀请新用户注册赠送积分活动 882195
科研通“疑难数据库(出版商)”最低求助积分说明 809208