三维术中超声成像中的脑肿瘤分割。

Segmentation of brain tumour in 3D Intraoperative Ultrasound imaging.

机构信息

Engineering Division (DICIS), Department of Electronics Engineering, University of Guanajuato, Campus Irapuato-Salamanca, Salamanca, Mexico.

Innovation Center Computer Assisted Surgery (ICCAS), Leipzig University, Leipzig, Germany.

出版信息

Int J Med Robot. 2021 Dec;17(6):e2320. doi: 10.1002/rcs.2320. Epub 2021 Aug 27.

DOI:10.1002/rcs.2320

PMID:34405533

Abstract

BACKGROUND

Intraoperative ultrasound (iUS), using a navigation system and preoperative magnetic resonance imaging (pMRI), supports the surgeon intraoperatively in identifying tumour margins. Therefore, visual tumour enhancement can be supported by efficient segmentation methods.

METHODS

A semi-automatic and two registration-based segmentation methods are evaluated to extract brain tumours from 3D-iUS data. The registration-based methods estimated the brain deformation after craniotomy based on pMRI and 3D-iUS data. Both approaches use the normalised gradient field and linear correlation of linear combinations metrics. Proposed methods were evaluated on 66 B-mode and contrast-mode 3D-iUS data with metastasis and glioblastoma.

RESULTS

The semi-automatic segmentation achieved superior results with dice similarity index (DSI) values between [85.34, 86.79]% and contour mean distance values between [1.05, 1.11] mm for both modalities and tumour classes.

CONCLUSIONS

Better segmentation results were obtained for metastasis detection than glioblastoma, preferring 3D-intraoperative B-mode over 3D-intraoperative contrast-mode.

摘要

背景

术中超声（iUS）结合导航系统和术前磁共振成像（pMRI），可在术中协助外科医生识别肿瘤边界。因此，通过有效的分割方法可以支持视觉肿瘤增强。

方法

评估了一种半自动和两种基于配准的分割方法，以从 3D-iUS 数据中提取脑肿瘤。基于配准的方法基于 pMRI 和 3D-iUS 数据估计开颅术后的大脑变形。这两种方法都使用归一化梯度场和线性组合度量的线性相关性。所提出的方法在 66 个转移瘤和胶质母细胞瘤的 B 模式和对比模式 3D-iUS 数据上进行了评估。