Diffusion MRI and silver standard masks to improve CNN-based thalamus segmentation

Pinheiro, Gustavo Retuci; Brusini, Lorenza; Bajrami, Albulena; Pizzini, Francesca Benedetta; Calabrese, Massimiliano; Reis, Fabiano; Appenzeller, Simone; Menegaz, Gloria; Rittner, Letícia

doi:10.1117/12.2581895

Cited by 3 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This contradicts some research that supports the inclusion of silver-standard labels. [48][49][50] This may be due to the significant visual difference between FreeSurfer labels and manual annotation (Fig. 2).…”

Section: Discussionmentioning

confidence: 99%

Can incomplete silver standard labels improve performance of DTI-based volumetric segmentation of the corpus callosum?

Rodrigues

Carmo²,

Abreu³

et al. 2023

18th International Symposium on Medical Information Processing and Analysis

View full text Add to dashboard Cite

Corpus Callosum (CC) segmentation is required when the analysis from this structure is desirable. Many of these studies require the CC segmentation on diffusion tensor images (DTI). However, few methods perform segmentation directly in the DTI. Segmenting on DTI makes it possible to disregard the registration step after segmenting on T1 images. This work studies the possibility of improving automated segmentation of the CC using silver standard annotations. With incomplete silver standard annotations, limited to 5 or 7 central slices, experiments performed throughout this work were done to compare methods of pre-training and fine tuning in an attempt to translate silver standard knowledge to improved performance in 3D CC segmentation. Experiments include 3D and 2D U-Net as deep learning architectures. Results point to central limited silver standard annotations not being useful for improving the performance in gold standard 3D annotations. Our best method involved training a 3D U-Net with gold standards and post processing, achieving a 3D Dice of 83.33 Dice, surpassing 2D U-Net.

show abstract

Section: Discussionmentioning

confidence: 99%

Can incomplete silver standard labels improve performance of DTI-based volumetric segmentation of the corpus callosum?

Rodrigues

Carmo²,

Abreu³

et al. 2023

18th International Symposium on Medical Information Processing and Analysis

View full text Add to dashboard Cite

show abstract

“…There has been a significant amount of research in the field of brain tumor detection, including both traditional computer vision techniques [8][9][10][11][12][13] and deep learning-based approaches [17][18][19][20][21][22]. Traditional computer vision techniques for brain tumor detection often involve manual feature extraction and classification using techniques such as support vector machines (SVM), decision trees, and Random Forest [23,24]. In recent years, deep learning techniques such as convolutional neural networks (CNNs) have become increasingly popular for brain tumor detection due to their ability to automatically learn features from medical imaging data [25][26][27].…”

Section: Related Studiesmentioning

confidence: 99%

Efficient Brain Tumor Detection with Lightweight End-to-End Deep Learning Model

Hammad

ElAffendi

Ateya

et al. 2023

Cancers

View full text Add to dashboard Cite

In the field of medical imaging, deep learning has made considerable strides, particularly in the diagnosis of brain tumors. The Internet of Medical Things (IoMT) has made it possible to combine these deep learning models into advanced medical devices for more accurate and efficient diagnosis. Convolutional neural networks (CNNs) are a popular deep learning technique for brain tumor detection because they can be trained on vast medical imaging datasets to recognize cancers in new images. Despite its benefits, which include greater accuracy and efficiency, deep learning has disadvantages, such as high computing costs and the possibility of skewed findings due to inadequate training data. Further study is needed to fully understand the potential and limitations of deep learning in brain tumor detection in the IoMT and to overcome the obstacles associated with real-world implementation. In this study, we propose a new CNN-based deep learning model for brain tumor detection. The suggested model is an end-to-end model, which reduces the system’s complexity in comparison to earlier deep learning models. In addition, our model is lightweight, as it is built from a small number of layers compared to other previous models, which makes the model suitable for real-time applications. The optimistic findings of a rapid increase in accuracy (99.48% for binary class and 96.86% for multi-class) demonstrate that the new framework model has excelled in the competition. This study demonstrates that the suggested deep model outperforms other CNNs for detecting brain tumors. Additionally, the study provides a framework for secure data transfer of medical lab results with security recommendations to ensure security in the IoMT.

show abstract

Connectome-based schizophrenia prediction using structural connectivity - Deep Graph Neural Network(sc-DGNN)

Udayakumar,

Subhashini

2024

XST

View full text Add to dashboard Cite

Background: Connectome is understanding the complex organization of the human brain’s structural and functional connectivity is essential for gaining insights into cognitive processes and disorders. Objective: To improve the prediction accuracy of brain disorder issues, the current study investigates dysconnected subnetworks and graph structures associated with schizophrenia. Method: By using the proposed structural connectivity-deep graph neural network (sc-DGNN) model and compared with machine learning (ML) and deep learning (DL) models.This work attempts to focus on eighty-eight subjects of diffusion magnetic resonance imaging (dMRI), three classical ML, and five DL models. Result: The structural connectivity-deep graph neural network (sc-DGNN) model is proposed to effectively predict dysconnectedness associated with schizophrenia and exhibits superior performance compared to traditional ML and DL (GNNs) methods in terms of accuracy, sensitivity, specificity, precision, F1-score, and Area under receiver operating characteristic (AUC). Conclusion: The classification task on schizophrenia using structural connectivity matrices and experimental results showed that linear discriminant analysis (LDA) performed 72% accuracy rate in ML models and sc-DGNN performed at a 93% accuracy rate in DL models to distinguish between schizophrenia and healthy patients.

show abstract

Diffusion MRI and silver standard masks to improve CNN-based thalamus segmentation

Cited by 3 publications

References 0 publications

Can incomplete silver standard labels improve performance of DTI-based volumetric segmentation of the corpus callosum?

Can incomplete silver standard labels improve performance of DTI-based volumetric segmentation of the corpus callosum?

Efficient Brain Tumor Detection with Lightweight End-to-End Deep Learning Model

Connectome-based schizophrenia prediction using structural connectivity - Deep Graph Neural Network(sc-DGNN)

Contact Info

Product

Resources

About