Automated Cerebral Infarct Detection on Computed Tomography Images Based on Deep Learning

The limited accuracy of cerebral infarct detection on CT images caused by the low contrast of CT hinders the desirable application of CT as a first-line diagnostic modality for screening of cerebral infarct. This research was aimed at utilizing convolutional neural network to enhance the accuracy of automated cerebral infarct detection on CT images. The CT images underwent a series of preprocessing steps mainly to enhance the contrast inside the parenchyma, adjust the orientation, spatially normalize the images to the CT template, and create a t-score map for each patient. The input format of the convolutional neural network was the t-score matrix of a 16 × 16-pixel patch. Non-infarcted and infarcted patches were selected from the t-score maps, on which data augmentation was conducted to generate more patches for training and testing the proposed convolutional neural network. The convolutional neural network attained a 93.9% patch-wise detection accuracy in the test set. The proposed method offers prompt and accurate cerebral infarct detection on CT images. It renders a frontline detection modality of ischemic stroke on an emergent or regular basis.

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Improvement in the Convolutional Neural Network for Computed Tomography Images

Applied Sciences ◽

10.3390/app11041505 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1505

Author(s):

Keisuke Manabe ◽

Yusuke Asami ◽

Tomonari Yamada ◽

Hiroyuki Sugimori

Keyword(s):

Neural Network ◽

Computed Tomography ◽

Convolutional Neural Network ◽

Ct Images ◽

Classification Model ◽

Max Pooling ◽

Computed Tomography Images ◽

Input Size ◽

Filter Size

Background and purpose. This study evaluated a modified specialized convolutional neural network (CNN) to improve the accuracy of medical images. Materials and Methods. We defined computed tomography (CT) images as belonging to one of the following 10 classes: head, neck, chest, abdomen, and pelvis with and without contrast media, with 10,000 images per class. We modified the CNN based on the AlexNet with an input size of 512 × 512. We resized the filter sizes of the convolution layer and max pooling. Using these modified CNNs, various models were created and evaluated. The improved CNN was evaluated to classify the presence or absence of the pancreas in the CT images. We compared the overall accuracy, which was calculated from images not used for training, to that of the ResNet. Results. The overall accuracies of the most improved CNN and ResNet in the 10 classes were 94.8% and 89.3%, respectively. The filter sizes of the improved CNN for the convolution layer were (13, 13), (7, 7), (5, 5), (5, 5), and (5, 5) in order from the first layer, and that of max-pooling was (7, 7). The calculation times of the most improved CNN and ResNet were 56 and 120 min, respectively. Regarding the classification of the pancreas, the overall accuracies of the most improved CNN and ResNet were 75.75% and 58.25%, respectively. The calculation times of the most improved CNN and ResNet were 36 and 55 min, respectively. Conclusion. By optimizing the filter size of the convolution layer and max-pooling of 512 × 512 images, we quickly obtained a highly accurate medical image classification model. This improved CNN can be useful for classifying lesions and anatomies for related diagnostic aid applications.

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Intracranial Hemorrhage Detection in Head CT Using Double-Branch Convolutional Neural Network, Support Vector Machine, and Random Forest

Applied Sciences ◽

10.3390/app10217577 ◽

2020 ◽

Vol 10 (21) ◽

pp. 7577

Author(s):

Agata Sage ◽

Pawel Badura

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Random Forest ◽

Convolutional Neural Network ◽

Intracranial Hemorrhage ◽

Support Vector ◽

Detection Accuracy ◽

Final Decision ◽

Brain Hemorrhage ◽

Computed Tomography Images

Brain hemorrhage is a severe threat to human life, and its timely and correct diagnosis and treatment are of great importance. Multiple types of brain hemorrhage are distinguished depending on the location and character of bleeding. The main division covers five subtypes: subdural, epidural, intraventricular, intraparenchymal, and subarachnoid hemorrhage. This paper presents an approach to detect these intracranial hemorrhage types in computed tomography images of the head. The model trained for each hemorrhage subtype is based on a double-branch convolutional neural network of ResNet-50 architecture. It extracts features from two chromatic representations of the input data: a concatenation of the image normalized in different intensity windows and a stack of three consecutive slices creating a 3D spatial context. The joint feature vector is passed to the classifier to produce the final decision. We tested two tools: the support vector machine and the random forest. The experiments involved 372,556 images from 11,454 CT series of 9997 patients, with each image annotated with labels related to the hemorrhage subtypes. We validated deep networks from both branches of our framework and the model with either of two classifiers under consideration. The obtained results justify the use of a combination of double-source features with the random forest classifier. The system outperforms state-of-the-art methods in terms of F1 score. The highest detection accuracy was obtained in intraventricular (96.7%) and intraparenchymal hemorrhages (93.3%).

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Evaluating severity of white matter lesions from computed tomography images with convolutional neural network

Neuroradiology ◽

10.1007/s00234-020-02410-2 ◽

2020 ◽

Vol 62 (10) ◽

pp. 1257-1263

Author(s):

Johanna Pitkänen ◽

Juha Koikkalainen ◽

Tuomas Nieminen ◽

Ivan Marinkovic ◽

Sami Curtze ◽

...

Keyword(s):

Neural Network ◽

Computed Tomography ◽

White Matter ◽

Convolutional Neural Network ◽

Rating Scale ◽

Pearson Correlation ◽

Ct Images ◽

Magnetic Resonance Images ◽

Computed Tomography Images ◽

Image Pairs

Abstract Purpose Severity of white matter lesion (WML) is typically evaluated on magnetic resonance images (MRI), yet the more accessible, faster, and less expensive method is computed tomography (CT). Our objective was to study whether WML can be automatically segmented from CT images using a convolutional neural network (CNN). The second aim was to compare CT segmentation with MRI segmentation. Methods The brain images from the Helsinki University Hospital clinical image archive were systematically screened to make CT-MRI image pairs. Selection criteria for the study were that both CT and MRI images were acquired within 6 weeks. In total, 147 image pairs were included. We used CNN to segment WML from CT images. Training and testing of CNN for CT was performed using 10-fold cross-validation, and the segmentation results were compared with the corresponding segmentations from MRI. Results A Pearson correlation of 0.94 was obtained between the automatic WML volumes of MRI and CT segmentations. The average Dice similarity index validating the overlap between CT and FLAIR segmentations was 0.68 for the Fazekas 3 group. Conclusion CNN-based segmentation of CT images may provide a means to evaluate the severity of WML and establish a link between CT WML patterns and the current standard MRI-based visual rating scale.

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Automated Ventricular System Segmentation in Paediatric Patients Treated for Hydrocephalus Using Deep Learning Methods

BioMed Research International ◽

10.1155/2019/3059170 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Michał Klimont ◽

Mateusz Flieger ◽

Jacek Rzeszutek ◽

Joanna Stachera ◽

Aleksandra Zakrzewska ◽

...

Keyword(s):

Neural Network ◽

Data Augmentation ◽

Ct Images ◽

Policy Transfer ◽

Training Data ◽

Intraobserver Variability ◽

Practical Applications ◽

Brain Scans ◽

Rate Policy ◽

Ct Brain

Hydrocephalus is a common neurological condition that can have traumatic ramifications and can be lethal without treatment. Nowadays, during therapy radiologists have to spend a vast amount of time assessing the volume of cerebrospinal fluid (CSF) by manual segmentation on Computed Tomography (CT) images. Further, some of the segmentations are prone to radiologist bias and high intraobserver variability. To improve this, researchers are exploring methods to automate the process, which would enable faster and more unbiased results. In this study, we propose the application of U-Net convolutional neural network in order to automatically segment CT brain scans for location of CSF. U-Net is a neural network that has proven to be successful for various interdisciplinary segmentation tasks. We optimised training using state of the art methods, including “1cycle” learning rate policy, transfer learning, generalized dice loss function, mixed float precision, self-attention, and data augmentation. Even though the study was performed using a limited amount of data (80 CT images), our experiment has shown near human-level performance. We managed to achieve a 0.917 mean dice score with 0.0352 standard deviation on cross validation across the training data and a 0.9506 mean dice score on a separate test set. To our knowledge, these results are better than any known method for CSF segmentation in hydrocephalic patients, and thus, it is promising for potential practical applications.

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Pancreatic cancer tumor analysis in CT images using patch-based multi-resolution convolutional neural network

Biomedical Signal Processing and Control ◽

10.1016/j.bspc.2021.102652 ◽

2021 ◽

Vol 68 ◽

pp. 102652

Author(s):

Vahid Asadpour ◽

Rex A. Parker ◽

Patrick R. Mayock ◽

Samuel E. Sampson ◽

Wansu Chen ◽

...

Keyword(s):

Neural Network ◽

Pancreatic Cancer ◽

Convolutional Neural Network ◽

Ct Images ◽

Cancer Tumor

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Prediction of pneumonia COVID19 using a custom convolutional neural network with data augmentation

10.1063/5.0045329 ◽

2021 ◽

Author(s):

Budi Dwi Satoto ◽

Mohammad Imam Utoyo ◽

Riries Rulaningtyas

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Data Augmentation

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Morphological residual convolutional neural network (M-RCNN) for intelligent recognition of wear particles from artificial joints

Friction ◽

10.1007/s40544-021-0516-2 ◽

2021 ◽

Author(s):

Xiaobin Hu ◽

Jian Song ◽

Zhenhua Liao ◽

Yuhong Liu ◽

Jian Gao ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Data Augmentation ◽

Support Vector ◽

Wear Particles ◽

Artificial Joints ◽

Performance Deterioration ◽

Overall Performance ◽

Correct Category ◽

Intelligent Recognition

AbstractFinding the correct category of wear particles is important to understand the tribological behavior. However, manual identification is tedious and time-consuming. We here propose an automatic morphological residual convolutional neural network (M-RCNN), exploiting the residual knowledge and morphological priors between various particle types. We also employ data augmentation to prevent performance deterioration caused by the extremely imbalanced problem of class distribution. Experimental results indicate that our morphological priors are distinguishable and beneficial to largely boosting overall performance. M-RCNN demonstrates a much higher accuracy (0.940) than the deep residual network (0.845) and support vector machine (0.821). This work provides an effective solution for automatically identifying wear particles and can be a powerful tool to further analyze the failure mechanisms of artificial joints.

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