MRI radiomics-based interpretable model and nomogram for preoperative prediction of Ki-67 expression status in primary central nervous system lymphoma DOI Creative Commons

Endong Zhao,

Yunfeng Yang,

Miaomiao Bai

et al.

Frontiers in Medicine, Journal Year: 2024, Volume and Issue: 11

Published: June 27, 2024

Objectives To investigate the value of interpretable machine learning model and nomogram based on clinical factors, MRI imaging features, radiomic features to predict Ki-67 expression in primary central nervous system lymphomas (PCNSL). Materials methods images information 92 PCNSL patients were retrospectively collected, which divided into 53 cases training set 39 external validation according different medical centers. A 3D brain tumor segmentation was trained nnU-NetV2, two prediction models, Random Forest (RF) incorporating SHapley Additive exPlanations (SHAP) method multivariate logistic regression, proposed for task status prediction. Results The mean dice Similarity Coefficient (DSC) score 0.85. On task, AUC RF 0.84 (95% CI:0.81, 0.86; p < 0.001), a 3% improvement compared nomogram. Delong test showed that z statistic difference between models 1.901, corresponding 0.057. In addition, SHAP analysis Rad-Score made significant contribution decision. Conclusion this study, we developed used an preoperative patients, improved task. Clinical relevance statement represents degree active cell proliferation is important prognostic parameter associated with outcomes. Non-invasive accurate level preoperatively plays role targeting treatment selection patient stratification management thereby improving prognosis.

Language: Английский

Deep learning for medical image segmentation: State-of-the-art advancements and challenges DOI Creative Commons
Md. Eshmam Rayed,

S. M. Sajibul Islam,

Sadia Islam Niha

et al.

Informatics in Medicine Unlocked, Journal Year: 2024, Volume and Issue: 47, P. 101504 - 101504

Published: Jan. 1, 2024

Image segmentation, a crucial process of dividing images into distinct parts or objects, has witnessed remarkable advancements with the emergence deep learning (DL) techniques. The use layers in neural networks, like object form recognition higher and basic edge identification lower layers, markedly improved quality accuracy image segmentation. Consequently, DL using picture segmentation become commonplace, video analysis, facial recognition, etc. Grasping applications, algorithms, current performance, challenges are for advancing DL-based medical However, there's lack studies delving latest state-of-the-art developments this field. Therefore, survey aimed to thoroughly explore most recent applications encompassing an in-depth analysis various commonly used datasets, pre-processing techniques algorithms. This study also investigated advancement done by analyzing their results experimental details. Finally, discussed future research directions Overall, provides comprehensive insight covering its application domains, model exploration, results, challenges, directions—a valuable resource multidisciplinary studies.

Language: Английский

Citations

44
Enhancing medical image segmentation with MA-UNet: a multi-scale attention framework DOI
Hongzhi Li, Z. Y. Ren,

Zhu Guoqing

et al.

The Visual Computer, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 5, 2025

Language: Английский

Citations

3
A review of deep learning segmentation methods for carotid artery ultrasound images DOI
Qinghua Huang, Haozhe Tian,

Lizhi Jia

et al.

Neurocomputing, Journal Year: 2023, Volume and Issue: 545, P. 126298 - 126298

Published: May 5, 2023

Language: Английский

Citations

40
MFFSP: Multi-scale feature fusion scene parsing network for landslides detection based on high-resolution satellite images DOI
Penglei Li, Yi Wang, Tongzhen Si

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 127, P. 107337 - 107337

Published: Oct. 27, 2023

Language: Английский

Citations

23
Role of Artificial Intelligence in Medical Image Analysis: A Review of Current Trends and Future Directions DOI
Xin Li, Lei Zhang, Jingsi Yang

et al.

Journal of Medical and Biological Engineering, Journal Year: 2024, Volume and Issue: 44(2), P. 231 - 243

Published: April 1, 2024

Language: Английский

Citations

12
C2FTFNet: Coarse-to-fine transformer network for joint optic disc and cup segmentation DOI
Yugen Yi, Yan Jiang, Bin Zhou

et al.

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 164, P. 107215 - 107215

Published: July 5, 2023

Language: Английский

Citations

21
Clinical assessment of deep learning-based uncertainty maps in lung cancer segmentation DOI
Federica Carmen Maruccio, Wietse S.C. Eppinga, Max-Heinrich Laves

et al.

Physics in Medicine and Biology, Journal Year: 2024, Volume and Issue: 69(3), P. 035007 - 035007

Published: Jan. 3, 2024

. Prior to radiation therapy planning, accurate delineation of gross tumour volume (GTVs) and organs at risk (OARs) is crucial. In the current clinical practice, performed manually by oncologists, which time-consuming prone large inter-observer variability. With advent deep learning (DL) models, automated contouring has become possible, speeding up procedures assisting clinicians. However, these tools are currently used in clinic mostly for OARs, since systems not reliable yet GTVs. To improve reliability systems, researchers have started exploring topic probabilistic neural networks. there still limited knowledge practical implementation such networks real settings.

Language: Английский

Citations

8
Multi-scale coal and gangue detection in dense state based on improved Mask RCNN DOI
Xi Wang, Shuang Wang, Yongcun Guo

et al.

Measurement, Journal Year: 2023, Volume and Issue: 221, P. 113467 - 113467

Published: Aug. 25, 2023

Language: Английский

Citations

14
Artificial Intelligence in Obstetric Anomaly Scan: Heart and Brain DOI Creative Commons
Iuliana-Alina Enache,

Cătălina Iovoaica-Rămescu,

Ștefan Gabriel Ciobanu

et al.

Life, Journal Year: 2024, Volume and Issue: 14(2), P. 166 - 166

Published: Jan. 23, 2024

Background: The ultrasound scan represents the first tool that obstetricians use in fetal evaluation, but sometimes, it can be limited by mobility or position, excessive thickness of maternal abdominal wall, presence post-surgical scars on wall. Artificial intelligence (AI) has already been effectively used to measure biometric parameters, automatically recognize standard planes and for disease diagnosis, which helps conventional imaging methods. usage information, images, a machine learning program create an algorithm capable assisting healthcare providers reducing workload, duration examination, increasing correct diagnosis capability. recent remarkable expansion electronic medical records diagnostic coincides with enormous success algorithms image identification tasks. Objectives: We aim review most relevant studies based deep anomaly evaluation complex systems (heart brain), enclose frequent anomalies.

Language: Английский

Citations

5
CrossTransUnet: A New Computationally Inexpensive Tumor Segmentation Model for Brain MRI DOI Creative Commons
Andrés Anaya-Isaza, Leonel Mera-Jiménez, Álvaro Fernández-Quilez

et al.

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 27066 - 27085

Published: Jan. 1, 2023

Brain tumors are usually fatal diseases with low life expectancies due to the organs they affect, even if benign. Diagnosis and treatment of these challenging tasks, for experienced physicians experts, heterogeneity tumor cells. In recent years, advances in deep learning (DL) methods have been integrated aid diagnosis, detection, segmentation brain neoplasms. However, is a computationally expensive process, typically based on convolutional neural networks (CNNs) UNet framework. While has shown promising results, new models developments can be incorporated into conventional architecture improve performance. this research, we propose three new, inexpensive, inspired by Transformers. These designed 4-stage encoder-decoder structure implement our cross-attention model, along separable convolution layers, avoid loss dimensionality activation maps reduce computational cost while maintaining high The attention model different configurations modifying transition encoder, decoder blocks. proposed evaluated against classical network, showing that differences up an order magnitude number training parameters. Additionally, one outperforms UNet, achieving significantly less time Dice Similarity Coefficient (DSC) 94%, ensuring effectiveness segmentation.

Language: Английский

Citations

11