论文标题
3D重建和分割无MRI神经病理学的解剖照片
3D Reconstruction and Segmentation of Dissection Photographs for MRI-free Neuropathology
论文作者
论文摘要
神经影像学与神经病理相关性(NTNC)有望使病理学的显微镜特征转移到MRI的体内成像,最终增强临床护理。 NTNC传统上需要进行体积的MRI扫描,并在死亡前不久就获得了。不幸的是,离体MRI很困难且昂贵,并且最近很少有足够质量的预列扫描。为了弥合这一差距,我们向3D重建和段的方法介绍了大脑解剖照片的完整大脑图像量,这些照片是在许多脑库和神经病理学部门常规获取的。 3D重建是通过使用MRI以外的参考量的联合注册框架实现的。该体积可以代表手头的样品(例如表面3D扫描)或一般种群(概率地图集)。此外,我们还提出了一种贝叶斯方法,将3D重建的照相量分为36个神经解剖结构,该结构在照片内部和跨照片内对非均匀的亮度具有稳健性。我们使用骰子分数和音量相关性评估了24个大脑数据集上的方法。结果表明,在许多体积分析中,解剖摄影是离体MRI的有效替代者,为无MRI NTNC开辟了一条途径,包括回顾性数据。该代码可从https://github.com/htregidgo/dissectionphotovolumes获得。
Neuroimaging to neuropathology correlation (NTNC) promises to enable the transfer of microscopic signatures of pathology to in vivo imaging with MRI, ultimately enhancing clinical care. NTNC traditionally requires a volumetric MRI scan, acquired either ex vivo or a short time prior to death. Unfortunately, ex vivo MRI is difficult and costly, and recent premortem scans of sufficient quality are seldom available. To bridge this gap, we present methodology to 3D reconstruct and segment full brain image volumes from brain dissection photographs, which are routinely acquired at many brain banks and neuropathology departments. The 3D reconstruction is achieved via a joint registration framework, which uses a reference volume other than MRI. This volume may represent either the sample at hand (e.g., a surface 3D scan) or the general population (a probabilistic atlas). In addition, we present a Bayesian method to segment the 3D reconstructed photographic volumes into 36 neuroanatomical structures, which is robust to nonuniform brightness within and across photographs. We evaluate our methods on a dataset with 24 brains, using Dice scores and volume correlations. The results show that dissection photography is a valid replacement for ex vivo MRI in many volumetric analyses, opening an avenue for MRI-free NTNC, including retrospective data. The code is available at https://github.com/htregidgo/DissectionPhotoVolumes.