Prediction of Deformations on Elastic Objects Using an LSTM Model DOI

Lisandro Vazquez-Aguilar,

Verónica E. Arriola-Ríos

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 59 - 72

Published: Jan. 1, 2024

Language: Английский

Development and validation of fully automated robust deep learning models for multi-organ segmentation from whole-body CT images DOI Creative Commons
Yazdan Salimi, Isaac Shiri, Zahra Mansouri

et al.

Physica Medica, Journal Year: 2025, Volume and Issue: 130, P. 104911 - 104911

Published: Feb. 1, 2025

This study aimed to develop a deep-learning framework generate multi-organ masks from CT images in adult and pediatric patients. A dataset consisting of 4082 ground-truth manual segmentation various databases, including 300 cases, were collected. In strategy#1, the provided by public databases split into training (90%) testing (10% each database named subset #1) cohort. The set was used train multiple nnU-Net networks five-fold cross-validation (CV) for 26 separate organs. next step, trained models strategy #1 missing organs entire dataset. generated data then model CV (strategy#2). Models' performance evaluated terms Dice coefficient (DSC) other well-established image metrics. lowest DSC strategy#1 0.804 ± 0.094 adrenal glands while average > 0.90 achieved 17/26 strategy#2 (0.833 0.177) obtained pancreas, whereas 13/19 For all mutual included #2, our outperformed TotalSegmentator both strategies. addition, on #3. Our with significant variability different producing acceptable results making it well-suited implementation clinical setting.

Language: Английский

Citations

2
Automated segmentation in pelvic radiotherapy: A comprehensive evaluation of ATLAS-, machine learning-, and deep learning-based models DOI Creative Commons
B. Bordigoni, S. Trivellato, Roberto Pellegrini

et al.

Physica Medica, Journal Year: 2024, Volume and Issue: 125, P. 104486 - 104486

Published: Aug. 3, 2024

Language: Английский

Citations

4
From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non‐Invasive Precision Medicine in Cancer Patients DOI Creative Commons
Yusheng Guo,

Tianxiang Li,

Bingxin Gong

et al.

Advanced Science, Journal Year: 2024, Volume and Issue: 12(2)

Published: Nov. 13, 2024

Abstract With the increasing demand for precision medicine in cancer patients, radiogenomics emerges as a promising frontier. Radiogenomics is originally defined methodology associating gene expression information from high‐throughput technologies with imaging phenotypes. However, advancements medical imaging, omics technologies, and artificial intelligence, both concept application of have significantly broadened. In this review, history enumerated, related five basic workflows their applications across tumors, role AI radiogenomics, opportunities challenges tumor heterogeneity, immune microenvironment. The positron emission tomography multi‐omics studies also discussed. Finally, faced by clinical transformation, along future trends field

Language: Английский

Citations

4
Deep learning-based segmentation of gallbladder cancer on abdominal computed tomography scans: a multicenter study DOI
Pankaj Gupta,

N Dutta,

Ajay Tomar

et al.

Abdominal Radiology, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Language: Английский

Citations

0
Hybrid U-Net Model with Visual Transformers for Enhanced Multi-Organ Medical Image Segmentation DOI Creative Commons
Pengfei Jiang, Wufeng Liu, Feihu Wang

et al.

Information, Journal Year: 2025, Volume and Issue: 16(2), P. 111 - 111

Published: Feb. 6, 2025

Medical image segmentation is an essential process that facilitates the precise extraction and localization of diseased areas from medical pictures. It can provide clear quantifiable information to support clinicians in making final decisions. However, due lack explicit modeling global relationships CNNs, they are unable fully use long-range dependencies among several locations. In this paper, we propose a novel model extract local semantic features images by utilizing CNN visual transformer encoder. important note self-attention mechanism treats 2D as 1D sequence patches, which potentially disrupt image’s inherent spatial structure. Therefore, utilized structure using attention large kernel attention, added residual convolutional module (RCAM) multi-scale fusion convolution (MFC) into decoder. They help better capture crucial fine details improve detail accuracy effects. On synapse multi-organ (Synapse) automated cardiac diagnostic challenge (ACDC) datasets, our performed than previous models, demonstrating it more robust segmentation.

Language: Английский

Citations

0
Stratifying Prostate Cancer: A Comprehensive Framework for Staging and Treatment Planning Classification DOI Open Access

Avishek Rauniyar,

Benjaram M. Reddy,

S. Remya

et al.

Procedia Computer Science, Journal Year: 2025, Volume and Issue: 259, P. 356 - 365

Published: Jan. 1, 2025

Language: Английский

Citations

0
Challenges and opportunities to integrate artificial intelligence in radiation oncology: a narrative review DOI Creative Commons
C. Jeong, Y. M. Goh, Jungwon Kwak

et al.

The Ewha Medical Journal, Journal Year: 2024, Volume and Issue: 47(4)

Published: Sept. 12, 2024

Artificial intelligence (AI) is rapidly transforming various medical fields, including radiation oncology. This review explores the integration of AI into oncology, highlighting both challenges and opportunities. can improve precision, efficiency, outcomes therapy by optimizing treatment planning, enhancing image analysis, facilitating adaptive therapy, enabling predictive analytics. Through analysis large datasets to identify optimal parameters, automate complex tasks, reduce planning time, accuracy. In AI-driven techniques enhance tumor detection segmentation processing data from CT, MRI, PET scans enable precise delineation. beneficial because it allows real-time adjustments plans based on changes in patient anatomy size, thereby improving accuracy effectiveness. Predictive analytics using historical predict potential complications, guiding clinical decision-making more personalized strategies. Challenges adoption oncology include ensuring quality quantity, achieving interoperability standardization, addressing regulatory ethical considerations, overcoming resistance implementation. Collaboration among researchers, clinicians, scientists, industry stakeholders crucial these obstacles. By challenges, drive advancements care operational efficiencies. presents an overview current state insights future directions for research practice.

Language: Английский

Citations

3
Deep learning application for abdominal organs segmentation on 0.35 T MR-Linac images DOI Creative Commons
You Zhou, Alain Lalande,

Cédric Chevalier

et al.

Frontiers in Oncology, Journal Year: 2024, Volume and Issue: 13

Published: Jan. 8, 2024

Linear accelerator (linac) incorporating a magnetic resonance (MR) imaging device providing enhanced soft tissue contrast is particularly suited for abdominal radiation therapy. In particular, accurate segmentation tumors and organs at risk (OARs) required the treatment planning becoming possible. Currently, this performed manually by oncologists. This process very time consuming subject to inter intra operator variabilities. work, deep learning based automatic solutions were investigated OARs on 0.35 T MR-images. One hundred twenty one sets of MR images their corresponding ground truth segmentations collected used work. The interest included liver, kidneys, spinal cord, stomach duodenum. Several UNet models have been trained in 2D (the Classical UNet, ResAttention EfficientNet nnUNet). best model was then with 3D strategy order investigate possible improvements. Geometrical metrics such as Dice Similarity Coefficient (DSC), Intersection over Union (IoU), Hausdorff Distance (HD) analysis calculated volumes (thanks Bland-Altman plot) evaluate results. nnUNet mode achieved performance, DSC scores stomach, duodenum 0.96 ± 0.01, 0.91 0.02, 0.83 0.10, 0.69 0.15, respectively. matching IoU 0.92 0.84 0.04, 0.54 0.16 0.72 0.13. HD 13.0 6.0 mm, 16.0 6.6 3.3 0.7 35.0 33.0 42.0 24.0 mm. followed same behavior. Although results not optimal, these findings imply potential clinical application from MR-Linac.

Language: Английский

Citations

2
Advancing Multi-organ and Pan-Cancer Segmentation in Abdominal CT Scans Through Scale-Aware and Self-attentive Modulation DOI
Pengju Lyu, Junchen Xiong, Wei Fang

et al.

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 84 - 101

Published: Jan. 1, 2024

Language: Английский

Citations

1
Prediction of Deformations on Elastic Objects Using an LSTM Model DOI

Lisandro Vazquez-Aguilar,

Verónica E. Arriola-Ríos

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 59 - 72

Published: Jan. 1, 2024

Language: Английский

Citations

0