Planar single plane area determination is a viable substitute for total volumetry of CSF and brain in childhood hydrocephalus.

BACKGROUND: In the treatment of childhood hydrocephalus, 3D volumetry seems to have many advantages over classical planar index measurements for dedicated monitoring of changes in cerebrospinal fluid and brain volume. Nevertheless, this method requires extensive technical effort and access to the complete three-dimensional data set. Against this background, we evaluated the possibility of planar area determination in a single plane and the correlation to volumetry. METHODS: 138 routinely performed true FISP MRI sequences (1 mm isovoxel) were analyzed retrospectively in 68 patients with pediatric hydrocephalus. After preprocessing, the 3D-data sets were skull stripped to estimate the inner skull volume. A 2-class segmentation into different tissue types (brain matter and CSF) was performed, and the volumes of CSF (VCSF) and brain matter (VBrain) were calculated. A plane at the level of the foramina of Monro was manually identified in the ac-pc oriented data. In this plane, the areas of brain (ABrain) and CSF (ACSF) in cm2 were calculated and used for further correlation analysis. RESULTS: Mean VCSF was 340 ± 145 cm3 and VBrain 1173 ± 254 cm3. In the selected plane, ACSF was 26 ± 14 cm2, and ABrain was 107 ± 25 cm2. There was a very strong positive correlation between both ACSF and VCSF (r = 0.895) and between ABrain and VBrain (r = 0.846). The prediction equations for VBrain and VCSF were highly significant. CONCLUSION: Planar area determination of brain and CSF correlates excellently with both VCSF and VBrain. Thus, areas can serve as a surrogate marker for total brain and CSF volumes for a quantitated objective tracking of changes during treatment of childhood hydrocephalus.

Semantic segmentation of cerebrospinal fluid and brain volume with a convolutional neural network in pediatric hydrocephalus-transfer learning from existing algorithms.

BACKGROUND: For the segmentation of medical imaging data, a multitude of precise but very specific algorithms exist. In previous studies, we investigated the possibility of segmenting MRI data to determine cerebrospinal fluid and brain volume using a classical machine learning algorithm. It demonstrated good clinical usability and a very accurate correlation of the volumes to the single area determination in a reproducible axial layer. This study aims to investigate whether these established segmentation algorithms can be transferred to new, more generalizable deep learning algorithms employing an extended transfer learning procedure and whether medically meaningful segmentation is possible. METHODS: Ninety-five routinely performed true FISP MRI sequences were retrospectively analyzed in 43 patients with pediatric hydrocephalus. Using a freely available and clinically established segmentation algorithm based on a hidden Markov random field model, four classes of segmentation (brain, cerebrospinal fluid (CSF), background, and tissue) were generated. Fifty-nine randomly selected data sets (10,432 slices) were used as a training data set. Images were augmented for contrast, brightness, and random left/right and X/Y translation. A convolutional neural network (CNN) for semantic image segmentation composed of an encoder and corresponding decoder subnetwork was set up. The network was pre-initialized with layers and weights from a pre-trained VGG 16 model. Following the network was trained with the labeled image data set. A validation data set of 18 scans (3289 slices) was used to monitor the performance as the deep CNN trained. The classification results were tested on 18 randomly allocated labeled data sets (3319 slices) and on a T2-weighted BrainWeb data set with known ground truth. RESULTS: The segmentation of clinical test data provided reliable results (global accuracy 0.90, Dice coefficient 0.86), while the CNN segmentation of data from the BrainWeb data set showed comparable results (global accuracy 0.89, Dice coefficient 0.84). The segmentation of the BrainWeb data set with the classical FAST algorithm produced consistent findings (global accuracy 0.90, Dice coefficient 0.87). Likewise, the area development of brain and CSF in the long-term clinical course of three patients was presented. CONCLUSION: Using the presented methods, we showed that conventional segmentation algorithms can be transferred to new advances in deep learning with comparable accuracy, generating a large number of training data sets with relatively little effort. A clinically meaningful segmentation possibility was demonstrated.

Automatic volumetry of cerebrospinal fluid and brain volume in severe paediatric hydrocephalus, implementation and clinical course after intervention.

BACKGROUND: In childhood hydrocephalus, both the amount of cerebrospinal fluid and the brain volume are relevant for the prognosis of the development and for therapy monitoring. Since classical planar measurements of ventricular size are subject to strong limitations, imprecise and neglect brain volume, 3D volumetry is most desirable. We used and evaluated the robust segmentation algorithms of the freely available FSL-toolbox in paediatric hydrocephalus patients before and after specific therapy. METHODS: Retrospectively 76 pre- and postoperative high-resolution T2-weighted MRI sequences (true FISP, 1 mm isovoxel) were analyzed in 38 patients with paediatric hydrocephalus (mean 4.4 ± 5.1 years) who underwent surgical treatment (ventriculo-peritoneal (VP) shunt n = 22, endoscopic third ventriculostomy (ETV) n = 16). After preprocessing, the 3D-datasets were skull stripped to estimate the inner skull surface. Following, a 2 class segmentation into different tissue types (brain matter and CSF) was performed. The volumes of CSF and brain were calculated. RESULTS: The method could be implemented in an automated fashion in all 76 MRIs. In the VP shunt cohort, the amount of CSF (p < 0.001) decreased. Consecutively brain volume increased significantly (p < 0.001). Following ETV, CSF volume (p = 0.019) decreased significantly (p = 0.012) although the reduction was less pronounced than after shunt implantation. Brain volume expanded (p = 0.02). CONCLUSION: A reliable automated segmentation of CSF and brain could be performed with the implemented algorithm. The method was able to track changes after therapy and detected significant differences in CSF and brain volumes after shunting and after ETV.