Mertzanidou Thomy, Hipwell John H, Reis Sara, Hawkes David J, Ehteshami Bejnordi Babak, Dalmis Mehmet, Vreemann Suzan, Platel Bram, van der Laak Jeroen, Karssemeijer Nico, Hermsen Meyke, Bult Peter, Mann Ritse
Centre for Medical Image Computing, University College London, WC1E 6BT, London, UK.
Diagnostic Image Analysis Group, Radboud University Medical Center, 6500 HB, Nijmegen, The Netherlands.
Med Phys. 2017 Mar;44(3):935-948. doi: 10.1002/mp.12077.
In breast imaging, radiological in vivo images, such as x-ray mammography and magnetic resonance imaging (MRI), are used for tumor detection, diagnosis, and size determination. After excision, the specimen is typically sliced into slabs and a small subset is sampled. Histopathological imaging of the stained samples is used as the gold standard for characterization of the tumor microenvironment. A 3D volume reconstruction of the whole specimen from the 2D slabs could facilitate bridging the gap between histology and in vivo radiological imaging. This task is challenging, however, due to the large deformation that the breast tissue undergoes after surgery and the significant undersampling of the specimen obtained in histology. In this work, we present a method to reconstruct a coherent 3D volume from 2D digital radiographs of the specimen slabs.
To reconstruct a 3D breast specimen volume, we propose the use of multiple target neighboring slices, when deforming each 2D slab radiograph in the volume, rather than performing pairwise registrations. The algorithm combines neighborhood slice information with free-form deformations, which enables a flexible, nonlinear deformation to be computed subject to the constraint that a coherent 3D volume is obtained. The neighborhood information provides adequate constraints, without the need for any additional regularization terms.
The volume reconstruction algorithm is validated on clinical mastectomy samples using a quantitative assessment of the volume reconstruction smoothness and a comparison with a whole specimen 3D image acquired for validation before slicing. Additionally, a target registration error of 5 mm (comparable to the specimen slab thickness of 4 mm) was obtained for five cases. The error was computed using manual annotations from four observers as gold standard, with interobserver variability of 3.4 mm. Finally, we illustrate how the reconstructed volumes can be used to map histology images to a 3D specimen image of the whole sample (either MRI or CT).
Qualitative and quantitative assessment has illustrated the benefit of using our proposed methodology to reconstruct a coherent specimen volume from serial slab radiographs. To our knowledge, this is the first method that has been applied to clinical breast cases, with the goal of reconstructing a whole specimen sample. The algorithm can be used as part of the pipeline of mapping histology images to ex vivo and ultimately in vivo radiological images of the breast.
在乳腺成像中,诸如乳腺X线摄影和磁共振成像(MRI)等放射学体内图像用于肿瘤检测、诊断和大小测定。切除后,标本通常被切成薄片,并抽取一小部分进行采样。染色样本的组织病理学成像被用作表征肿瘤微环境的金标准。从二维薄片对整个标本进行三维体积重建有助于弥合组织学与体内放射学成像之间的差距。然而,由于乳腺组织在手术后会发生较大变形,且组织学中获得的标本存在显著欠采样,这项任务具有挑战性。在这项工作中,我们提出了一种从标本薄片的二维数字射线照片重建连贯三维体积的方法。
为了重建三维乳腺标本体积,我们建议在对体积中的每个二维薄片射线照片进行变形时,使用多个目标相邻切片,而不是进行成对配准。该算法将相邻切片信息与自由形式变形相结合,从而能够在获得连贯三维体积的约束下计算灵活的非线性变形。相邻信息提供了足够的约束,无需任何额外的正则化项。
使用体积重建平滑度的定量评估以及与切片前获取用于验证的整个标本三维图像进行比较,对临床乳房切除样本验证了体积重建算法。此外,五例病例的目标配准误差为5毫米(与4毫米的标本薄片厚度相当)。该误差是使用四位观察者的手动标注作为金标准计算得出的,观察者间的变异性为3.4毫米。最后,我们说明了重建的体积如何用于将组织学图像映射到整个样本的三维标本图像(MRI或CT)。
定性和定量评估表明了使用我们提出的方法从连续薄片射线照片重建连贯标本体积的益处。据我们所知,这是第一种应用于临床乳腺病例的方法,目标是重建整个标本样本。该算法可作为将组织学图像映射到乳腺离体及最终体内放射学图像流程的一部分。