scholarly journals MR Images, Brain Lesions, and Deep Learning

2021 ◽  
Vol 11 (4) ◽  
pp. 1675
Author(s):  
Darwin Castillo ◽  
Vasudevan Lakshminarayanan ◽  
María José Rodríguez-Álvarez
Keyword(s):  
Deep Learning ◽  
Performance Metrics ◽  
Demyelinating Diseases ◽  
Computer Assisted ◽  
Dice Similarity Coefficient ◽  
Clinical Environment ◽  
Mr Images ◽  
Demyelinating Lesions ◽  
Brain Image Analysis

Medical brain image analysis is a necessary step in computer-assisted/computer-aided diagnosis (CAD) systems. Advancements in both hardware and software in the past few years have led to improved segmentation and classification of various diseases. In the present work, we review the published literature on systems and algorithms that allow for classification, identification, and detection of white matter hyperintensities (WMHs) of brain magnetic resonance (MR) images, specifically in cases of ischemic stroke and demyelinating diseases. For the selection criteria, we used bibliometric networks. Of a total of 140 documents, we selected 38 articles that deal with the main objectives of this study. Based on the analysis and discussion of the revised documents, there is constant growth in the research and development of new deep learning models to achieve the highest accuracy and reliability of the segmentation of ischemic and demyelinating lesions. Models with good performance metrics (e.g., Dice similarity coefficient, DSC: 0.99) were found; however, there is little practical application due to the use of small datasets and a lack of reproducibility. Therefore, the main conclusion is that there should be multidisciplinary research groups to overcome the gap between CAD developments and their deployment in the clinical environment.

scholarly journals MRI Images, Brain Lesions and Deep Learning

2021 ◽  
Author(s):  
Darwin Castillo ◽  
Vasudevan Lakshminarayanan ◽  
María José Rodríguez-Álvarez
Keyword(s):  
Deep Learning ◽  
Brain Mri ◽  
Demyelinating Diseases ◽  
Clinical Environment ◽  
The Past ◽  
Demyelinating Lesions ◽  
Brain Image Analysis ◽  
Aided Diagnosis ◽  
Constant Growth

Medical brain image analysis is a necessary step in the Computers Assisted /Aided Diagnosis (CAD) systems. Advancements in both hardware and software in the past few years have led to improved segmentation and classification of various diseases. In the present work, we review the published literature on systems and algorithms that allow for classification, identification, and detection of White Matter Hyperintensities (WMHs) of brain MRI images specifically in cases of ischemic stroke and demyelinating diseases. For the selection criteria, we used the bibliometric networks. Out of a total of 140 documents we selected 38 articles that deal with the main objectives of this study. Based on the analysis and discussion of the revised documents, there is constant growth in the research and proposal of new models of deep learning to achieve the highest accuracy and reliability of the segmentation of ischemic and demyelinating lesions. Models with indicators (Dice Score, DSC: 0.99) were found, however with little practical application due to the uses of small datasets and lack of reproducibility. Therefore, the main conclusion is to establish multidisciplinary research groups to overcome the gap between CAD developments and their complete utilization in the clinical environment.

Deep Learning-Based Binary Classification of ADHD Using Resting State MR Images

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Vikas Khullar ◽  
Karuna Salgotra ◽  
Harjit Pal Singh ◽  
Davinder Pal Sharma
Keyword(s):  
Deep Learning ◽  
Resting State ◽  
Binary Classification ◽  
Mr Images

scholarly journals Benchmarking feature quality assurance strategies for non-targeted metabolomics

2021 ◽  
Author(s):  
Yasin El Abiead ◽  
Maximilian Milford ◽  
Harald Schoeny ◽  
Mate Rusz ◽  
Reza M Salek ◽  
...  
Keyword(s):  
Quality Control ◽  
Quality Assurance ◽  
Liquid Chromatography ◽  
Deep Learning ◽  
Performance Metrics ◽  
Targeted Metabolomics ◽  
Mass Spec ◽  
Chromatographic Peaks ◽  
Resolution Mass

Automated data pre-processing (DPP) forms the basis of any liquid chromatography-high resolution mass spec-trometry-driven non-targeted metabolomics experiment. However, current strategies for quality control of this im-portant step have rarely been investigated or even discussed. We exemplified how reliable benchmark peak lists could be generated for eleven publicly available datasets acquired across different instrumental platforms. Moreover, we demonstrated how these benchmarks can be utilized to derive performance metrics for DPP and tested whether these metrics can be generalized for entire datasets. Relying on this principle, we cross-validated different strategies for quality assurance of DPP, including manual parameter adjustment, variance of replicate injection-based metrics, unsupervised clustering performance, automated parameter optimization, and deep learning-based classification of chromatographic peaks. Overall, we want to highlight the importance of assessing DPP performance on a regular basis.

scholarly journals Efficient few-shot machine learning for classification of EBSD patterns

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kevin Kaufmann ◽  
Hobson Lane ◽  
Xiao Liu ◽  
Kenneth S. Vecchio
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Transfer Learning ◽  
Performance Metrics ◽  
Materials Science ◽  
Electron Backscatter Diffraction ◽  
Point Group ◽  
Phase Identification ◽  
Classification Problems

AbstractDeep learning is quickly becoming a standard approach to solving a range of materials science objectives, particularly in the field of computer vision. However, labeled datasets large enough to train neural networks from scratch can be challenging to collect. One approach to accelerating the training of deep learning models such as convolutional neural networks is the transfer of weights from models trained on unrelated image classification problems, commonly referred to as transfer learning. The powerful feature extractors learned previously can potentially be fine-tuned for a new classification problem without hindering performance. Transfer learning can also improve the results of training a model using a small amount of data, known as few-shot learning. Herein, we test the effectiveness of a few-shot transfer learning approach for the classification of electron backscatter diffraction (EBSD) pattern images to six space groups within the $$\left( {4/m \overline {3} 2/m} \right)$$ 4 / m 3 ¯ 2 / m point group. Training history and performance metrics are compared with a model of the same architecture trained from scratch. In an effort to make this approach more explainable, visualization of filters, activation maps, and Shapley values are utilized to provide insight into the model’s operations. The applicability to real-world phase identification and differentiation is demonstrated using dual phase materials that are challenging to analyze with traditional methods.

scholarly journals Deep-learning framework and computer assisted fatty infiltration analysis for the supraspinatus muscle in MRI

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kyunghan Ro ◽  
Joo Young Kim ◽  
Heeseol Park ◽  
Baek Hwan Cho ◽  
In Young Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Fatty Infiltration ◽  
Computer Assisted ◽  
Dice Similarity Coefficient ◽  
Supraspinatus Muscle ◽  
Thresholding Method ◽  
Learning Framework ◽  
Otsu Thresholding ◽  
Shoulder Mri

AbstractOccupation ratio and fatty infiltration are important parameters for evaluating patients with rotator cuff tears. We analyzed the occupation ratio using a deep-learning framework and studied the fatty infiltration of the supraspinatus muscle using an automated region-based Otsu thresholding technique. To calculate the amount of fatty infiltration of the supraspinatus muscle using an automated region-based Otsu thresholding technique. The mean Dice similarity coefficient, accuracy, sensitivity, specificity, and relative area difference for the segmented lesion, measuring the similarity of clinician assessment and that of a deep neural network, were 0.97, 99.84, 96.89, 99.92, and 0.07, respectively, for the supraspinatus fossa and 0.94, 99.89, 93.34, 99.95, and 2.03, respectively, for the supraspinatus muscle. The fatty infiltration measure using the Otsu thresholding method significantly differed among the Goutallier grades (Grade 0; 0.06, Grade 1; 4.68, Grade 2; 20.10, Grade 3; 42.86, Grade 4; 55.79, p < 0.0001). The occupation ratio and fatty infiltration using Otsu thresholding demonstrated a moderate negative correlation (ρ = − 0.75, p < 0.0001). This study included 240 randomly selected patients who underwent shoulder magnetic resonance imaging (MRI) from January 2015 to December 2016. We used a fully convolutional deep-learning algorithm to quantitatively detect the fossa and muscle regions by measuring the occupation ratio of the supraspinatus muscle. Fatty infiltration was objectively evaluated using the Otsu thresholding method. The proposed convolutional neural network exhibited fast and accurate segmentation of the supraspinatus muscle and fossa from shoulder MRI, allowing automatic calculation of the occupation ratio. Quantitative evaluation using a modified Otsu thresholding method can be used to calculate the proportion of fatty infiltration in the supraspinatus muscle. We expect that this will improve the efficiency and objectivity of diagnoses by quantifying the index used for shoulder MRI.

scholarly journals Auto-segmentation of the parotid glands on MR images of head and neck cancer patients with deep learning strategies

2020 ◽  
Author(s):  
Jennifer P. Kieselmann ◽  
Clifton D. Fuller ◽  
Oliver J. Gurney-Champion ◽  
Uwe Oelfke
Keyword(s):  
Head And Neck Cancer ◽  
Deep Learning ◽  
Head And Neck ◽  
Neck Cancer ◽  
Dice Similarity Coefficient ◽  
Observer Variability ◽  
Parotid Glands ◽  
Mr Images ◽  
Inter Observer Variability ◽  
Mri Guided

AbstractAdaptive online MRI-guided radiotherapy of head and neck cancer requires the reliable segmentation of the parotid glands as important organs at risk in clinically acceptable time frames. This can hardly be achieved by manual contouring. We therefore designed deep learning-based algorithms which automatically perform this task.Imaging data comprised two datasets: 27 patient MR images (T1-weighted and T2-weighted) and eight healthy volunteer MR images (T2-weighted), together with manually drawn contours by an expert. We used four different convolutional neural network (CNN) designs that each processed the data differently, varying the dimensionality of the input. We assessed the segmentation accuracy calculating the Dice similarity coefficient (DSC), Hausdorff distance (HD) and mean surface distance (MSD) between manual and auto-generated contours. We benchmarked the developed methods by comparing to the inter-observer variability and to atlas-based segmentation. Additionally, we assessed the generalisability, strengths and limitations of deep learning-based compared to atlas-based methods in the independent volunteer test dataset.With a mean DSC of 0.85± 0.11 and mean MSD of 1.82 ±1.94 mm, a 2D CNN could achieve an accuracy comparable to that of an atlas-based method (DSC: 0.85 ±0.05, MSD: 1.67 ±1.21 mm) and the inter-observer variability (DSC: 0.84 ±0.06, MSD: 1.50 ±0.77 mm) but considerably faster (<1s v.s. 45 min). Adding information (adjacent slices, fully 3D or multi-modality) did not further improve the accuracy. With additional preprocessing steps, the 2D CNN was able to generalise well for the fully independent volunteer dataset (DSC: 0.79 ±0.10, MSD: 1.72 ±0.96 mm)We demonstrated the enormous potential for the application of CNNs to segment the parotid glands for online MRI-guided radiotherapy. The short computation times render deep learning-based methods suitable for online treatment planning workflows.

scholarly journals A Novel and Robust Approach to Detect Tuberculosis Using Transfer Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Omar Faruk ◽  
Eshan Ahmed ◽  
Sakil Ahmed ◽  
Anika Tabassum ◽  
Tahia Tazin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Computer Assisted ◽  
Promising Technique ◽  
Suggested Approach ◽  
X Ray ◽  
Chest X Ray ◽  
Deep Learning Model

Deep learning has emerged as a promising technique for a variety of elements of infectious disease monitoring and detection, including tuberculosis. We built a deep convolutional neural network (CNN) model to assess the generalizability of the deep learning model using a publicly accessible tuberculosis dataset. This study was able to reliably detect tuberculosis (TB) from chest X-ray images by utilizing image preprocessing, data augmentation, and deep learning classification techniques. Four distinct deep CNNs (Xception, InceptionV3, InceptionResNetV2, and MobileNetV2) were trained, validated, and evaluated for the classification of tuberculosis and nontuberculosis cases using transfer learning from their pretrained starting weights. With an F1-score of 99 percent, InceptionResNetV2 had the highest accuracy. This research is more accurate than earlier published work. Additionally, it outperforms all other models in terms of reliability. The suggested approach, with its state-of-the-art performance, may be helpful for computer-assisted rapid TB detection.

scholarly journals Evaluation of multi-task learning in deep learning-based positioning classification of mandibular third molars

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Shintaro Sukegawa ◽  
Tamamo Matsuyama ◽  
Futa Tanaka ◽  
Takeshi Hara ◽  
Kazumasa Yoshii ◽  
...  
Keyword(s):  
Deep Learning ◽  
Performance Metrics ◽  
Maxillofacial Surgery ◽  
Effect Sizes ◽  
Medium Effect ◽  
Third Molars ◽  
Single Task ◽  
Task Learning ◽  
Mandibular Third Molars

AbstractPell and Gregory, and Winter’s classifications are frequently implemented to classify the mandibular third molars and are crucial for safe tooth extraction. This study aimed to evaluate the classification accuracy of convolutional neural network (CNN) deep learning models using cropped panoramic radiographs based on these classifications. We compared the diagnostic accuracy of single-task and multi-task learning after labeling 1330 images of mandibular third molars from digital radiographs taken at the Department of Oral and Maxillofacial Surgery at a general hospital (2014–2021). The mandibular third molar classifications were analyzed using a VGG 16 model of a CNN. We statistically evaluated performance metrics [accuracy, precision, recall, F1 score, and area under the curve (AUC)] for each prediction. We found that single-task learning was superior to multi-task learning (all p < 0.05) for all metrics, with large effect sizes and low p-values. Recall and F1 scores for position classification showed medium effect sizes in single and multi-task learning. To our knowledge, this is the first deep learning study to examine single-task and multi-task learning for the classification of mandibular third molars. Our results demonstrated the efficacy of implementing Pell and Gregory, and Winter’s classifications for specific respective tasks.

scholarly journals Bidirectional ConvLSTMXNet for Brain Tumor Segmentation of MR Images

2021 ◽  
Vol 15 (1) ◽  
pp. 37-42
Author(s):  
M. Ravikumar ◽  
B.J. Shivaprasad
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Tumor Segmentation ◽  
Mr Images ◽  
Mr Image ◽  
Brain Tumor Segmentation ◽  
Data Set ◽  
Tumour Segmentation ◽  
Brain Tumour Segmentation

In recent years, deep learning based networks have achieved good performance in brain tumour segmentation of MR Image. Among the existing networks, U-Net has been successfully applied. In this paper, it is propose deep-learning based Bidirectional Convolutional LSTM XNet (BConvLSTMXNet) for segmentation of brain tumor and using GoogLeNet classify tumor &amp; non-tumor. Evaluated on BRATS-2019 data-set and the results are obtained for classification of tumor and non-tumor with Accuracy: 0.91, Precision: 0.95, Recall: 1.00 &amp; F1-Score: 0.92. Similarly for segmentation of brain tumor obtained Accuracy: 0.99, Specificity: 0.98, Sensitivity: 0.91, Precision: 0.91 &amp; F1-Score: 0.88.

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