An evaluation of performance measures for arterial brain vessel segmentation.

BACKGROUND: Arterial brain vessel segmentation allows utilising clinically relevant information contained within the cerebral vascular tree. Currently, however, no standardised performance measure is available to evaluate the quality of cerebral vessel segmentations. Thus, we developed a performance measure selection framework based on manual visual scoring of simulated segmentation variations to find the most suitable measure for cerebral vessel segmentation. METHODS: To simulate segmentation variations, we manually created non-overlapping segmentation errors common in magnetic resonance angiography cerebral vessel segmentation. In 10 patients, we generated a set of approximately 300 simulated segmentation variations for each ground truth image. Each segmentation was visually scored based on a predefined scoring system and segmentations were ranked based on 22 performance measures common in the literature. The correlation of visual scores with performance measure rankings was calculated using the Spearman correlation coefficient. RESULTS: The distance-based performance measures balanced average Hausdorff distance (rank = 1) and average Hausdorff distance (rank = 2) provided the segmentation rankings with the highest average correlation with manual rankings. They were followed by overlap-based measures such as Dice coefficient (rank = 7), a standard performance measure in medical image segmentation. CONCLUSIONS: Average Hausdorff distance-based measures should be used as a standard performance measure in evaluating cerebral vessel segmentation quality. They can identify more relevant segmentation errors, especially in high-quality segmentations. Our findings have the potential to accelerate the validation and development of novel vessel segmentation approaches.

Temporal Shape Changes of Pedicle screw-rod Constructs After Lumbar Interbody Fusion.

STUDY DESIGN: Retrospective multicenter study. OBJECTIVE: To examine the shape change of screw-rod constructs over time following short-segment lumbar interbody fusion and to clarify its relationship to clinical characteristics. SUMMARY OF BACKGROUND DATA: No study has focused on the shape change of screw-rod constructs after short-segment fusion and its clinical implications. METHODS: One hundred and eight patients who had single-level lumbar interbody fusion with pedicle screws and cages were enrolled. Three-dimensional (3D) images of screw-rod constructs were generated from baseline CT on the day after surgery and follow-up CT, and were superposed on the right and left side, respectively, using the iterative closest point algorithm. The shape change was quantitatively assessed by computing the median distance between the 3D images, which was defined as the shape change value. Among the five time-course categories of follow-up CT (≤1 month, 2-3 months, 4-6 months, 7-12 months, ≥13 months), the shape change values were compared. The relationships between the shape change values and clinical characteristics, such as age, CT-derived vertebral bone mineral density, screw and rod materials, and postoperative interbody fusion status, cage subsidence, and screw loosening, were evaluated. RESULTS: A total of 237 follow-up CTs were included (≤1 month [34 scans], 2-3 months [33 scans], 4-6 months [80 scans], 7-12 months [48 scans], ≥13 months [42 scans]) because many patients underwent multiple follow-up CTs. There were significant differences in shape change values among the time-course categories (P<0.001 in Kruskal-Wallis test). Most shape changes occurred within 6 months postoperatively, with no significant changes observed at 7 months or more. There were no significant relationships between the shape change values and each clinical characteristic. CONCLUSION: The temporal shape changes of screw-rod constructs following short-segment lumbar interbody fusion progressed up to 6 months after surgery but not significantly thereafter.

Prediction of hematoma expansion in spontaneous intracerebral hemorrhage using a multimodal neural network.

Hematoma expansion occasionally occurs in patients with intracerebral hemorrhage (ICH), associating with poor outcome. Multimodal neural networks incorporating convolutional neural network (CNN) analysis of images and neural network analysis of tabular data are known to show promising results in prediction and classification tasks. We aimed to develop a reliable multimodal neural network model that comprehensively analyzes CT images and clinical variables to predict hematoma expansion. We retrospectively enrolled ICH patients at four hospitals between 2017 and 2021, assigning patients from three hospitals to the training and validation dataset and patients from one hospital to the test dataset. Admission CT images and clinical variables were collected. CT findings were evaluated by experts. Three types of models were developed and trained: (1) a CNN model analyzing CT images, (2) a multimodal CNN model analyzing CT images and clinical variables, and (3) a non-CNN model analyzing CT findings and clinical variables with machine learning. The models were evaluated on the test dataset, focusing first on sensitivity and second on area under the receiver operating curve (AUC). Two hundred seventy-three patients (median age, 71 years [59-79]; 159 men) in the training and validation dataset and 106 patients (median age, 70 years [62-82]; 63 men) in the test dataset were included. Sensitivity and AUC of a CNN model were 1.000 (95% confidence interval [CI] 0.768-1.000) and 0.755 (95% CI 0.704-0.807); those of a multimodal CNN model were 1.000 (95% CI 0.768-1.000) and 0.799 (95% CI 0.749-0.849); and those of a non-CNN model were 0.857 (95% CI 0.572-0.982) and 0.733 (95% CI 0.625-0.840). We developed a multimodal neural network model incorporating CNN analysis of CT images and neural network analysis of clinical variables to predict hematoma expansion in ICH. The model was externally validated and showed the best performance of all the models.

On the usage of average Hausdorff distance for segmentation performance assessment: hidden error when used for ranking.

Average Hausdorff distance is a widely used performance measure to calculate the distance between two point sets. In medical image segmentation, it is used to compare ground truth images with segmentations allowing their ranking. We identified, however, ranking errors of average Hausdorff distance making it less suitable for applications in segmentation performance assessment. To mitigate this error, we present a modified calculation of this performance measure that we have coined "balanced average Hausdorff distance". To simulate segmentations for ranking, we manually created non-overlapping segmentation errors common in magnetic resonance angiography cerebral vessel segmentation as our use-case. Adding the created errors consecutively and randomly to the ground truth, we created sets of simulated segmentations with increasing number of errors. Each set of simulated segmentations was ranked using both performance measures. We calculated the Kendall rank correlation coefficient between the segmentation ranking and the number of errors in each simulated segmentation. The rankings produced by balanced average Hausdorff distance had a significantly higher median correlation (1.00) than those by average Hausdorff distance (0.89). In 200 total rankings, the former misranked 52 whilst the latter misranked 179 segmentations. Balanced average Hausdorff distance is more suitable for rankings and quality assessment of segmentations than average Hausdorff distance.

COVID-19-Associated Encephalitis Mimicking Glial Tumor.

BACKGROUND: Reports on neurologic manifestations of coronavirus disease 2019 (COVID-19) have attracted broad attention. We present an unusual case of COVID-19-associated encephalitis mimicking a glial tumor. CASE DESCRIPTION: A 35-year-old woman presented with headache and seizures. T2 fluid-attenuated inverse recovery imaging showed hyperintensities in the left temporal lobe. Magnetic resonance spectroscopy showed an elevated choline peak. Imaging findings were suggestive of high-grade glioma. Antiepileptic medication failed to achieve seizure control. A left anterior temporal lobectomy was performed. The patient had no postoperative deficits, and her symptoms completely improved. Histologic examination revealed encephalitis. Postoperatively, our patient tested positive for COVID-19. CONCLUSIONS: Our case raises awareness of neurologic manifestations of the disease and their potential to mimic glial tumors. For prompt diagnosis and prevention of transmission, clinicians should consider COVID-19 in patients with similar presentation.

Multiple Large-Size Cystic Cerebral Cavernomas.

BACKGROUND: Cerebral cavernomas are vascular malformations characterized by networks of abnormally dilated capillaries. They typically present as nodules with mixed signal intensity and a surrounding hemosiderin rim on magnetic resonance imaging. They may occur as multiple lesions in the autosomal-dominant familial form. In rare cases, cavernomas may form cystic masses, mimicking other pathologies. CASE DESCRIPTION: A 35-year-old man presented with recurrent seizures, aphasia, and gait disturbance with onset at age 14 years. He had previously undergone surgical drainage of multiple cysts across the brain with suspected parasitic infection. On magnetic resonance imaging, 22 cystic lesions were seen across the brain. A large cyst was located in the midline cerebellum, compressing the fourth ventricle. Occipital craniotomy and transvermian dissection allowed total resection of the cyst along with its wall. The postoperative course was uneventful and symptoms progressively improved. Histological analysis revealed cavernoma. Three more surgeries were performed for removal of large supratentorial cavernomas. CONCLUSIONS: In patients with cystic lesions of the brain, the neurosurgeon should consider the possibility of cavernoma. Total excision along with the cyst wall is crucial for timely diagnosis and therapy.

A U-Net Deep Learning Framework for High Performance Vessel Segmentation in Patients With Cerebrovascular Disease.

Brain vessel status is a promising biomarker for better prevention and treatment in cerebrovascular disease. However, classic rule-based vessel segmentation algorithms need to be hand-crafted and are insufficiently validated. A specialized deep learning method-the U-net-is a promising alternative. Using labeled data from 66 patients with cerebrovascular disease, the U-net framework was optimized and evaluated with three metrics: Dice coefficient, 95% Hausdorff distance (95HD) and average Hausdorff distance (AVD). The model performance was compared with the traditional segmentation method of graph-cuts. Training and reconstruction was performed using 2D patches. A full and a reduced architecture with less parameters were trained. We performed both quantitative and qualitative analyses. The U-net models yielded high performance for both the full and the reduced architecture: A Dice value of ~0.88, a 95HD of ~47 voxels and an AVD of ~0.4 voxels. The visual analysis revealed excellent performance in large vessels and sufficient performance in small vessels. Pathologies like cortical laminar necrosis and a rete mirabile led to limited segmentation performance in few patients. The U-net outperfomed the traditional graph-cuts method (Dice ~0.76, 95HD ~59, AVD ~1.97). Our work highly encourages the development of clinically applicable segmentation tools based on deep learning. Future works should focus on improved segmentation of small vessels and methodologies to deal with specific pathologies.