Automated Detection of Regions of Interest for Brain Perfusion MR Images

Images with abnormal brain anatomy produce problems for automatic segmentation techniques, and as a result poor ROI detection affects both quantitative measurements and visual assessment of perfusion data. This paper presents a new approach for fully automated and relatively accurate ROI detection from dynamic susceptibility contrast perfusion magnetic resonance and can therefore be applied excellently in the perfusion analysis. In the proposed approach the segmentation output is a binary mask of perfusion ROI that has zero values for air pixels, pixels that represent non-brain tissues, and cerebrospinal fluid pixels. The process of binary mask producing starts with extracting low intensity pixels by thresholding. Optimal low-threshold value is solved by obtaining intensity pixels information from the approximate anatomical brain location. Holes filling algorithm and binary region growing algorithm are used to remove falsely detected regions and produce region of only brain tissues. Further, CSF pixels extraction is provided by thresholding of high intensity pixels from region of only brain tissues. Each time-point image of the perfusion sequence is used for adjustment of CSF pixels location. The segmentation results were compared with the manual segmentation performed by experienced radiologists, considered as the reference standard for evaluation of proposed approach. On average of 120 images the segmentation results have a good agreement with the reference standard. All detected perfusion ROIs were deemed by two experienced radiologists as satisfactory enough for clinical use. The results show that proposed approach is suitable to be used for perfusion ROI detection from DSC head scans. Segmentation tool based on the proposed approach can be implemented as a part of any automatic brain image processing system for clinical use

Impact of perfusion ROI detection to the quality of CBV perfusion map

The object of research in this study is quality of CBV perfusion map, considering detection of perfusion ROI as a key component in processing of dynamic susceptibility contrast magnetic resonance images of a human head. CBV map is generally accepted to be the best among others to evaluate location and size of stroke lesions and angiogenesis of brain tumors. Its poor accuracy can cause failed results for both quantitative measurements and visual assessment of cerebral blood volume. The impact of perfusion ROI detection on the quality of maps was analyzed through comparison of maps produced from threshold and reference images of the same datasets from 12 patients with cerebrovascular disease. Brain perfusion ROI was placed to exclude low intensity (air and non-brain tissues regions) and high intensity (cerebrospinal fluid regions) pixels. Maps were produced using area under the curve and deconvolution methods. For both methods compared maps were primarily correlational according to Pearson correlation analysis: r=0.8752 and r=0.8706 for area under the curve and deconvolution, respectively, p 2.2 10-16. In spite of this, for both methods scatter plots had data points associated with missed blood regions and regression lines indicated presence of scale and offset errors for maps produced from threshold images. Obtained results indicate that thresholding is an ineffective way to detect brain perfusion ROI, which usage can cause degradation of CBV map quality. Perfusion ROI detection should be standardized and accepted into validation protocols of new systems for perfusion data analysis

Impact of perfusion ROI detection to the quality of CBV perfusion map

The object of research in this study is quality of CBV perfusion map, considering detection of perfusion ROI as a key component in processing of dynamic susceptibility contrast magnetic resonance images of a human head. CBV map is generally accepted to be the best among others to evaluate location and size of stroke lesions and angiogenesis of brain tumors. Its poor accuracy can cause failed results for both quantitative measurements and visual assessment of cerebral blood volume. The impact of perfusion ROI detection on the quality of maps was analyzed through comparison of maps produced from threshold and reference images of the same datasets from 12 patients with cerebrovascular disease. Brain perfusion ROI was placed to exclude low intensity (air and non-brain tissues regions) and high intensity (cerebrospinal fluid regions) pixels. Maps were produced using area under the curve and deconvolution methods. For both methods compared maps were primarily correlational according to Pearson correlation analysis: r=0.8752 and r=0.8706 for area under the curve and deconvolution, respectively, p 2.2 10-16. In spite of this, for both methods scatter plots had data points associated with missed blood regions and regression lines indicated presence of scale and offset errors for maps produced from threshold images. Obtained results indicate that thresholding is an ineffective way to detect brain perfusion ROI, which usage can cause degradation of CBV map quality. Perfusion ROI detection should be standardized and accepted into validation protocols of new systems for perfusion data analysis

Detection of the arterial input function using DSC-MRI data

Accurate detection of arterial input function is a crucial step in obtaining perfusion hemodynamic parameters using dynamic susceptibility contrast-enhanced magnetic resonance imaging. It is required as input for perfusion quantification and has a great impact on the result of the deconvolution operation. To improve the reproducibility and reliability of arterial input function detection, several semi- or fully automatic methods have been proposed. This study provides an overview of the current state of the field of arterial input function detection. Methods most commonly used for semi- and fully automatic arterial input function detection are reviewed, and their advantages and disadvantages are listed

Brain tissues segmentation on MR perfusion images using CUSUM filter for boundary pixels

The fully automated and relatively accurate method of brain tissues segmentation on Т2-weighted magnetic resonance perfusion images is proposed. Segmentation with this method provides a possibility to obtain perfusion region of interest on images with abnormal brain anatomy that is very important for perfusion analysis. In the proposed method the result is presented as a binary mask, which marks two regions: brain tissues pixels with unity values and skull, extracranial soft tissue and background pixels with zero values. The binary mask is produced based on the location of boundary between two studied regions. Each boundary point is detected with CUSUM filter as a change point for iteratively accumulated points at time of moving on a sinusoidal-like path along the boundary from one region to another. The evaluation results for 20 clinical cases showed that proposed segmentation method could significantly reduce the time and efforts required to obtain desirable results for perfusion region of interest detection on Т2-weighted magnetic resonance perfusion images with abnormal brain anatomy

Нейронна мережа на основі глибокого навчання для отримання зон інтересу на T2*-зважених перфузійних зображеннях МРТ мозку

Brain region segmentation is usually the first step for dynamic susceptibility contrast perfusion analysis. Although manual segmentation is more accurate, it is a time-consuming and not sufficiently reproducible process. Clinicians still rely on manual segmentation especially for cases with abnormal brain anatomy, as removing brain parts or inclusion of non-brain tissues can be a potential source of falsely high or falsely low values of perfusion parameters. This study proposes an effective deep learning-based neural network for fully automatic segmentation of brain from non-brain tissues in T2*-weighted magnetic resonance images with abnormal brain anatomy. Our neural network architecture combines U-Net and ResNet with plugged spatial and channel squeeze and excitation attention modules into the ResNet backbone. The train, validation, and test processes are conducted on 32 three-dimensional volumes of different subjects from the TCGA glioblastoma multiforme collection. Four performance metrics are used in our experiments: Dice coefficient, sensitivity, specificity, and accuracy. Quantitative results (i.e., Dice coefficient of 0.9726 +/- 0.004, sensitivity of 0.9514+/-0.007, specificity of 0.9983+/-0.001, and accuracy of 0.9864+/-0.003) reveal that the proposed neural network architecture is efficient and accurate for brain segmentation. The obtained results also demonstrate that the training model using the proposed U-Net+ResNet architecture of the neural network provides the best Dice coefficient, specificity, and accuracy metric values compared to current methods under the same hardware conditions and using the same test dataset of magnetic resonance images of a human head with abnormal brain anatomy. Moreover, obtained results also indicate that the proposed U-Net+ResNet architecture of deep learning-based neural network could be good enough in a clinical setup to reduce the need for time-consuming and non-reproducible manual segmentation

Attention-based convolutional neural network for perfusion T2-weighted MR images preprocessing

Accurate skull-stripping is crucial preprocessing in dynamic susceptibility contrast-enhanced perfusion magnetic resonance data analysis. The presence of non-brain tissues impacts the perfusion parameters assessment. In this study, we propose different integration strategies for the spatial and channel squeeze and excitation attention mechanism into the baseline U-Net+ResNet neural network architecture to provide automatic skull-striping i.e., Standard scSE, scSE-PRE, scSE-POST, and scSE Identity strategies of plugging of scSE block into the ResNet backbone. We comprehensively investigate the performance of skull-stripping in Т2* weighted MR images with abnormal brain anatomy. The comparison that utilizing any of the proposed strategies provides the robustness of skull-stripping. However, the scSE-POST integration strategy provides the best result with an average Dice Coefficient of 0.9810 +/- 0.006

Research of fast initial response feature for brain segmentation on MR images with CUSUM filter

Perfusion region of interest for the brain can be detected effectively on DSC perfusion MR images with CUSUM filter. This detection is based on the filtering of brain boundary points among accumulated ones at the time of moving on some trajectory. The filtering of points is provided by the CUSUM decision function. Applying of fast initial response feature for CUSUM filtering scheme gives a more rapid response to the out-of-control situation, and, as a result, provides more accurate segmentation results achieved through reduced processing time. This study aimed to analyze the impact of fast initial response feature on the results of brain segmentation on DSC perfusion MR images with CUSUM filter