Comparison of CNN- and Transformer-based Architectures for Automated Oral Epithelium Segmentation on Whole Slide Images DOI

Napat Srisermphoak,

Panomwat Amornphimoltham, Risa Chaisuparat

et al.

Published: Oct. 28, 2023

Oral cancer is one of the most commonly found cancers worldwide. Epithelial Dysplasia (OED) an Potentially Malignant Disorder (OPMD) that can be characterized for preventive oral screening. The standard OED histological grading conducted via epithelial regions tissue biopsies. However, this procedure laborious, time-consuming, and subjective; consequently, it prone to variability due fatigue limited expertise. Therefore, study aims explore potential using Convolutional Neural Network (CNN) Transformer models automated epithelium segmentation algorithm directly from Whole Slide Images (WSIs). This approach reduce manual process support pathologists in activities. Accordingly, candidate architectures based on CNN are selected: UNet, ResNet50-UNet, VGG19-UNet, Swin-UNet, MISSFormer. These trained patch-based mitigate high computational cost caused by processing WSIs. results indicate optimized with ADAM optimizer, demonstrates best performance Intersection over Union (IoU) 0.82 Dice-Similarity Coefficient (DSC) 0.87. Furthermore, model achieves highest IoU DSC tissue-level prediction, scoring 0.88 0.94, respectively. According experiment, overlapping non-overlapping patching strategies perform similarly selected architectures. latter approach, hence, suggested efficiency. enhancing provide a reliable tool assisting pathologists.

Language: Английский

Advances in Deep Learning for Medical Image Analysis: A Comprehensive Investigation DOI
Rajeev Ranjan Kumar, S. Vishnu Shankar, Ronit Jaiswal

et al.

Journal of Statistical Theory and Practice, Journal Year: 2025, Volume and Issue: 19(1)

Published: Jan. 23, 2025

Language: Английский

Citations

2
UMamba Adjustment: Advancing GTV Segmentation for Head and Neck Cancer in MRI-Guided RT with UMamba and NnU-Net ResEnc Planner DOI Creative Commons
Jintao Ren, Kim Hochreuter, Jesper Folsted Kallehauge

et al.

Lecture notes in computer science, Journal Year: 2025, Volume and Issue: unknown, P. 123 - 135

Published: Jan. 1, 2025

Language: Английский

Citations

0
3D lymphoma segmentation on PET/CT images via multi‐scale information fusion with cross‐attention DOI Open Access
H. K. Huang,

Liheng Qiu,

Shenmiao Yang

et al.

Medical Physics, Journal Year: 2025, Volume and Issue: unknown

Published: March 20, 2025

Accurate segmentation of diffuse large B-cell lymphoma (DLBCL) lesions is challenging due to their complex patterns in medical imaging. Traditional methods often struggle delineate these accurately. This study aims develop a precise method for DLBCL using 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) and computed (CT) images. We propose 3D based on an encoder-decoder architecture. The encoder incorporates dual-branch design the shifted window transformer extract features from both PET CT modalities. To enhance feature integration, we introduce multi-scale information fusion (MSIF) module that performs cross-attention mechanisms with framework. A gated neural network within MSIF dynamically adjusts weights balance contributions each modality. model optimized dice similarity coefficient (DSC) loss function, minimizing discrepancies between prediction ground truth. Additionally, total metabolic tumor volume (TMTV) performed statistical analyses results. was trained validated private dataset 165 patients publicly available (autoPET) containing 145 PET/CT scans patients. Both datasets were analyzed five-fold cross-validation. On dataset, our achieved DSC 0.7512, sensitivity 0.7548, precision 0.7611, average surface distance (ASD) 3.61 mm, Hausdorff at 95th percentile (HD95) 15.25 mm. autoPET 0.7441, 0.7573, 0.7427, ASD 5.83 HD95 21.27 outperforming state-of-the-art (p < 0.05, t-test). For TMTV quantification, Pearson correlation coefficients 0.91 (private dataset) 0.86 observed, R2 values 0.89 0.75, respectively. Extensive ablation studies demonstrated module's contribution enhanced accuracy. presents effective automatic leverages complementary strengths demonstrates robust performance datasets, ensuring its reliability generalizability. Our provides clinicians more delineation, which can improve accuracy diagnostic interpretations assist treatment planning code proposed https://github.com/chenzhao2023/lymphoma_seg.

Language: Английский

Citations

0
SwinDAF3D: Pyramid Swin Transformers with Deep Attentive Features for Automated Finger Joint Segmentation in 3D Ultrasound Images for Rheumatoid Arthritis Assessment DOI Creative Commons
Jianwei Qiu, Grigorios M. Karageorgos, Xiaorui Peng

et al.

Bioengineering, Journal Year: 2025, Volume and Issue: 12(4), P. 390 - 390

Published: April 5, 2025

Rheumatoid arthritis (RA) is a chronic autoimmune disease that can cause severe joint damage and functional impairment. Ultrasound imaging has shown promise in providing real-time assessment of synovium inflammation associated with the early stages RA. Accurate segmentation region quantification inflammation-specific biomarkers are crucial for assessing grading However, automatic 3D ultrasound challenging due to ambiguous boundaries, variability shape, inhomogeneous intensity distribution. In this work, we introduce novel network architecture, Swin Transformers Deep Attentive Features (SwinDAF3D), which integrates into framework. The developed architecture leverages hierarchical structure shifted windows capture rich, multi-scale attentive contextual information, improving modeling long-range dependencies spatial hierarchies images. six-fold cross-validation study images RA patients’ finger joints (n = 72), our SwinDAF3D model achieved highest performance Dice Score (DSC) 0.838 ± 0.013, an Intersection over Union (IoU) 0.719 0.019, Surface (SDSC) 0.852 0.020, compared UNet (DSC: 0.742 0.025; IoU: 0.589 0.031; SDSC: 0.661 0.029), DAF3D 0.813 0.017; 0.689 0.022; 0.817 0.013), UNETR 0.808 0.678 0.032; 0.822 0.039), UNETR++ 0.810 0.014; 0.684 0.018; 0.829 0.027) TransUNet 0.818 0.013; 0.692 0.815 0.016) models. This ablation demonstrates effectiveness combining feature pyramid deep attention mechanism, accuracy ultrasound. advancement shows great enabling more efficient standardized screening using imaging.

Language: Английский

Citations

0
Dual‐way magnetic resonance image translation with transformer‐based adversarial network DOI
Wenxin Li, Jun Xia,

Weilin Gao

et al.

Medical Physics, Journal Year: 2025, Volume and Issue: unknown

Published: April 24, 2025

Abstract Background The magnetic resonance (MR) image translation model is designed to generate MR images of required sequence from the existing sequence. However, generalization performance generation models on external datasets tends be unsatisfactory due inconsistency in data distribution across different centers or scanners. Purpose aim this study propose a cross‐sequence synthesis that could high‐quality synthetic with high transferability for small‐sized datasets. Methods We proposed dual‐way using transformer‐based adversarial network (DMTrans) sequences. It integrates generative architecture an innovative discriminator design. shifted window‐based multi‐head self‐attention mechanism DMTrans enables efficient capture global and local features images. sequential dual‐scale distinguish generated at multi‐scale. Results pre‐trained bi‐directional T1/T2‐weighted dataset comprising 4229 slices. demonstrates superior baseline methods both qualitative quantitative measurements. SSIM, PSNR, MAE metrics T1 based T2 are 0.91 ± 0.04, 25.30 2.40, 24.65 10.46, while metric values 0.90 24.72 1.62, 23.28 7.40 opposite direction. Fine‐tuning then utilized adapt another public T1/T2/proton‐weighted (PD) images, so only 6 patients 500 slices adaptation achieve T1/T2, T1/PD, T2/PD results. Conclusions achieves state‐of‐the‐art conversion, which provide more information assisting clinical diagnosis treatment. also offered versatile solution needs data‐scarce conditions centers.

Language: Английский

Citations

0
Automatic head and neck tumor segmentation through deep learning and Bayesian optimization on three-dimensional medical images DOI

Zachariah Douglas,

Abdur Rahman, William N Duggar

et al.

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 192, P. 110309 - 110309

Published: May 15, 2025

Language: Английский

Citations

0
Head and Neck Cancer Segmentation in FDG PET Images: Performance Comparison of Convolutional Neural Networks and Vision Transformers DOI Creative Commons
Xiaofan Xiong, Bruce A. Smith, Stephen A. Graves

et al.

Tomography, Journal Year: 2023, Volume and Issue: 9(5), P. 1933 - 1948

Published: Oct. 18, 2023

Convolutional neural networks (CNNs) have a proven track record in medical image segmentation. Recently, Vision Transformers were introduced and are gaining popularity for many computer vision applications, including object detection, classification, Machine learning algorithms such as CNNs or subject to an inductive bias, which can significant impact on the performance of machine models. This is especially relevant segmentation applications where limited training data available, model’s bias should help it generalize well. In this work, we quantitatively assess two CNN-based (U-Net U-Net-CBAM) three popular Transformer-based network architectures (UNETR, TransBTS, VT-UNet) context HNC lesion volumetric [F-18] fluorodeoxyglucose (FDG) PET scans. For assessment, 272 FDG PET-CT scans clinical trial (ACRIN 6685) utilized, includes total 650 lesions (primary: secondary: 378). The used highly diverse representative use. analysis, several error metrics utilized. achieved Dice coefficient ranged from 0.833 0.809 with best being by approaches. U-Net-CBAM, utilizes spatial channel attention, showed advantages smaller compared standard U-Net. Furthermore, our results provide some insight regarding features specific application. addition, highlight need utilize primary well secondary derive clinically estimates avoiding biases.

Language: Английский

Citations

6
Multi-task reconstruction network for synthetic diffusion kurtosis imaging: Predicting neoadjuvant chemoradiotherapy response in locally advanced rectal cancer DOI

Qiong Ma,

Zonglin Liu, Jiadong Zhang

et al.

European Journal of Radiology, Journal Year: 2024, Volume and Issue: 174, P. 111402 - 111402

Published: March 2, 2024

Language: Английский

Citations

2
Segmentation-Free Outcome Prediction from Head and Neck Cancer PET/CT Images: Deep Learning-Based Feature Extraction from Multi-Angle Maximum Intensity Projections (MA-MIPs) DOI Open Access
Amirhosein Toosi, Isaac Shiri, Habib Zaidi

et al.

Cancers, Journal Year: 2024, Volume and Issue: 16(14), P. 2538 - 2538

Published: July 14, 2024

We introduce an innovative, simple, effective segmentation-free approach for survival analysis of head and neck cancer (HNC) patients from PET/CT images. By harnessing deep learning-based feature extraction techniques multi-angle maximum intensity projections (MA-MIPs) applied to Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) images, our proposed method eliminates the need manual segmentations regions-of-interest (ROIs) such as primary tumors involved lymph nodes. Instead, a state-of-the-art object detection model is trained utilizing CT images perform automatic cropping anatomical area, instead only lesions or nodes on PET volumes. A pre-trained convolutional neural network backbone then utilized extract features MA-MIPs obtained 72 multi-angel axial rotations cropped These extracted multiple projection views volumes are aggregated fused, employed recurrence-free cohort 489 HNC patients. The outperforms best performing target dataset task analysis. circumventing delineation malignancies FDG PET-CT dependency subjective interpretations highly enhances reproducibility method. code this work publicly released.

Language: Английский

Citations

2
Multi-modal tumor segmentation methods based on deep learning: a narrative review DOI Open Access
Hengzhi Xue, Yudong Yao, Yueyang Teng

et al.

Quantitative Imaging in Medicine and Surgery, Journal Year: 2023, Volume and Issue: 14(1), P. 1122 - 1140

Published: Dec. 29, 2023

Automatic tumor segmentation is a critical component in clinical diagnosis and treatment. Although single-modal imaging provides useful information, multi-modal more comprehensive understanding of the tumor. Multi-modal has been an essential topic medical image processing. With remarkable performance deep learning (DL) methods analysis, based on DL attracted significant attention. This study aimed to provide overview recent DL-based methods.

Language: Английский

Citations

5