Automated hippocampal segmentation algorithms evaluated in stroke patients
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Abstract Deep learning segmentation algorithms can produce reproducible results in a matter of seconds. However, their application to more complex datasets is uncertain and may fail in the presence of severe structural abnormalities—such as those commonly seen in stroke patients. In this investigation, six recent, deep learning-based hippocampal segmentation algorithms were tested on 641 stroke patients of a multicentric, open-source dataset ATLAS 2.0. The comparisons of the volumes showed that the methods are not interchangeable with concordance correlation coefficients from 0.266 to 0.816. While the segmentation algorithms demonstrated an overall good performance (volumetric similarity [VS] 0.816 to 0.972, DICE score 0.786 to 0.921, and Hausdorff distance [HD] 2.69 to 6.34), no single out-performing algorithm was identified: FastSurfer performed best in VS, QuickNat in DICE and average HD, and Hippodeep in HD. Segmentation performance was significantly lower for ipsilesional segmentation, with a decrease in performance as a function of lesion size due to the pathology-based domain shift. Only QuickNat showed a more robust performance in volumetric similarity. Even though there are many pre-trained segmentation methods, it is important to be aware of the possible decrease in performance for the segmentation results on the lesion side due to the pathology-based domain shift. The segmentation algorithm should be selected based on the research question and the evaluation parameter needed. More research is needed to improve current hippocampal segmentation methods.Keywords:
Hausdorff distance
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Dice
In this paper, we present a novel approach for segmentation of Unruptured Intracranial Aneurysms (UIAs) in Time-of-Flight magnetic resonance angiographies (TOF-MRAs). We propose, 1) a U-Net based pure convolutional neural network architecture (U-ConvNeXt) mimicing self attention and capturing global spatial information as in Vision Transformers, 2) a novel combined loss function of Dice Loss and modified Hausdorff Distance (Focal Hausdorff Loss) which optimizes region localization and penalizes for spatial similarity between ground-truth and prediction masks, 3) a task-specific Transfer Learning approach leveraging pre-trained models of Brain Tumour Segmentation on large datasets. We first train and evaluate our model on the ADAM dataset released by MICCAI which comprises of 113 TOF-MRA scans of 78 unique patients with 93 scans having at least one UIA. The aneurysms differ significantly in size, location and appearance with huge imbalance in background to foreground ratio making segmentation a highly challenging task. On the holdout set, we get a Dice Score Coefficient of 0.87 and a Hausdorff Distance of 1.91 from our best model which is 12% higher than the current SOTA. We also evaluate our model on another independent holdout set comprising of 38 TOF-MRA scans from Lausanne University Hospital and get a baseline Dice Score coefficient of 0.84 and a Hausdorff Distance of 2.15 for comparison with future works. The aneurysms with diameter size less than 3mm are the most challenging to segment with a Dice Score Coefficient of 0.76 and the simplest with size greater than 7mm with a Dice Score Coefficient of 0.95.
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In the website framework if the navigation is not efficient then user may worry, so it is very significant to enhance the user navigation.Performance of the websites relies on their information and functionality requirements of their users.The development of website is designed using needs of users.Thus it is a big task to build a web page with the efficient navigations.Hence unique method is suggested using customer navigations information to re-link the websites.Reorientation of whole web page may raise the complications for the acquainted user and it may irritate them.The restrictions of current system are it contributes all the applicant hyperlinks even if it is essential for individual user.Because of this the framework of web page enhancement has been improved.Thus we suggested the system which finds out the maximum hyperlinks which are necessary for the user.We suggested the system which is successfully signify the navigation of the websites.This system uses the new hyperlinks for helping the framework of websites.System used Dice's coefficient, we enhance the web page framework so that user can achieve to the focus on page quicker.
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Dice loss is widely used for medical image segmentation, and many improvement loss functions based on such loss have been proposed. However, further Dice loss improvements are still possible. In this study, we reconsidered the use of Dice loss and discovered that Dice loss can be rewritten in the loss function using the cosine similarity through a simple equation transformation. Using this knowledge, we present a novel t-vMF Dice loss based on the t-vMF similarity instead of the cosine similarity. Based on the t-vMF similarity, our proposed Dice loss is formulated in a more compact similarity loss function than the original Dice loss. Furthermore, we present an effective algorithm that automatically determines the parameter $\kappa$ for the t-vMF similarity using a validation accuracy, called Adaptive t-vMf Dice loss. Using this algorithm, it is possible to apply more compact similarities for easy classes and wider similarities for difficult classes, and we are able to achieve an adaptive training based on the accuracy of the class. Through experiments conducted on four datasets using a five-fold cross validation, we confirmed that the Dice score coefficient (DSC) was further improved in comparison with the original Dice loss and other loss functions.
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Short-axis MRI segmentation of the right ventricle plays an important role in assessing the structure and function of the right ventricle. However, RV segmentation is a challenge due to its complex crescent shape. In this paper, we propose a deep learning-based method for segmenting RV using the registration of the right ventricular shape model. The RV shape probability model is constructed using training samples. Next, aU-Net is trained using the shape prior probability by employing the registration technique. The shape model is registered to the network's predictive results to estimate a shape probability map, and a loss is defined as the Kullback-Leibler divergence between the prediction result and the shape probability map and the Kullback-Leibler divergence between the predictive result and the Ground-truth. The experimental results obtained from the cardiac automatic diagnosis challenge-medical imaging calculation and computer-aided intervention (ACDC-MICCAI) 2017 dataset show that the average 3D dice coefficient is 0.919, and the average 3D Hausdorff distance is 10.71mm. Our network has also been verified in the MICCAI2012 right ventricle segmentation challenge(RVSC) dataset. The average dice coefficient is 0.865, and the Hausdorff distance is 6. 10mm. The evaluation results show that our network outperforms the state-of-art methods in several evaluation indicators.
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Effective image segmentation of cerebral structures is fundamental to 3-dimensional techniques such as augmented reality. To be clinically viable, segmentation algorithms should be fully automatic and easily integrated in existing digital infrastructure. We created a fully automatic adaptive-meshing-based segmentation system for T1-weighted magnetic resonance images (MRI) to automatically segment the complete ventricular system, running in a cloud-based environment that can be accessed on an augmented reality device. This study aims to assess the accuracy and segmentation time of the system by comparing it to a manually segmented ground truth dataset.A ground truth (GT) dataset of 46 contrast-enhanced and non-contrast-enhanced T1-weighted MRI scans was manually segmented. These scans also were uploaded to our system to create a machine-segmented (MS) dataset. The GT data were compared with the MS data using the Sørensen-Dice similarity coefficient and 95% Hausdorff distance to determine segmentation accuracy. Furthermore, segmentation times for all GT and MS segmentations were measured.Automatic segmentation was successful for 45 (98%) of 46 cases. Mean Sørensen-Dice similarity coefficient score was 0.83 (standard deviation [SD] = 0.08) and mean 95% Hausdorff distance was 19.06 mm (SD = 11.20). Segmentation time was significantly longer for the GT group (mean = 14405 seconds, SD = 7089) when compared with the MS group (mean = 1275 seconds, SD = 714) with a mean difference of 13,130 seconds (95% confidence interval 10,130-16,130).The described adaptive meshing-based segmentation algorithm provides accurate and time-efficient automatic segmentation of the ventricular system from T1 MRI scans and direct visualization of the rendered surface models in augmented reality.
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Purpose: Deformable image registration between CT and MRI remains a challenge due to variability in contrast, homogeneity, and field of view. This study aims to determine whether an optimized Mutual Information (MI) or Gradient Magnitude (GM) metric provides favorable results in the head and neck region. Methods: Head and neck CT and MR images were acquired for 14 patients as part of an MRI simulation study with a dual echo mDixon (in phase) T1 fast field echo sequence. Each pair of scans is registered with MRI as the reference image using the GM and MI metrics, where MI was optimized by controlling histogram parameters. Results are observed visually, and quantitatively measured by comparing Dice coefficient and Hausdorff distance of manually contoured bone structures. Results: Using the optimized GM metric, the average Dice coefficient is 0.52 ± 0.16; the average Hausdorff distance is 7.5 ± 1.9 mm. Using the optimized MI metric, the average Dice coefficient is 0.55 ± 0.085; the average Hausdorff distance is 6.6 ± 1.4 mm. Conclusion: While both metrics performed similarly, the MI metric has a higher Dice coefficient than the GM metric, and a lower Hausdorff distance. This suggests image registration with the Mutual Information metric may be preferred.
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Automatic segmentation of medical images, such as computed tomography (CT) or magnetic resonance imaging (MRI), plays an essential role in efficient clinical diagnosis. While deep learning have gained popularity in academia and industry, more works have to be done to improve the performance for clinical practice. U-Net architecture, along with Dice coefficient optimization, has shown its effectiveness in medical image segmentation. Although it is an efficient measurement of the difference between the ground truth and the network's output, the Dice loss struggles to train with samples that do not contain targeted objects. While the situation is unusual in standard datasets, it is commonly seen in clinical data, where many training data available without the anomalies shown in the images, such as lesions and anatomic structures in some CTs/regions. In this paper, we propose a novel loss function - Stochastic Aggregated Dice Coefficient (SA Dice) and a modification of the network structure to improve its performance. Experimentally, in our own heart aorta CT dataset, our models beats the baseline by 4% in cross-validation Dice scores. In BRATS 2017 brain tumor segmentation challenge, the models also perform better than the state-of-the-art by approximately 2%.
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