Automatic Lung Cancer Segmentation in [18F]FDG PET/CT Using a Two-Stage Deep Learning Approach

Park, Jun-Young; Kang, Seung Kwan; Hwang, Donghwi; Choi, Hongyoon; Ha, Seunggyun; Seo, Jong Mo; Eo, Jae Seon; Lee, Jae Sung

doi:10.1007/s13139-022-00745-7

Cited by 20 publications

(8 citation statements)

References 36 publications

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“…Park et al [100] propose a two-stage U-Net architecture for automatic lung cancer segmentation in [18F]FDG PET/CT scans. The proposed method involves a global U-Net in Stage 1, which receives a 3D PET/CT volume as input and extracts the preliminary tumor area generating a 3D binary volume as output.…”

Section: Deep Learning Technics Using Proprietary Datasetsmentioning

confidence: 99%

Deep Machine Learning for Medical Diagnosis, Application to Lung Cancer Detection: A Review

Gayap,

Akhloufi

2024

BioMedInformatics

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Deep learning has emerged as a powerful tool for medical image analysis and diagnosis, demonstrating high performance on tasks such as cancer detection. This literature review synthesizes current research on deep learning techniques applied to lung cancer screening and diagnosis. This review summarizes the state-of-the-art in deep learning for lung cancer detection, highlighting key advances, limitations, and future directions. We prioritized studies utilizing major public datasets, such as LIDC, LUNA16, and JSRT, to provide a comprehensive overview of the field. We focus on deep learning architectures, including 2D and 3D convolutional neural networks (CNNs), dual-path networks, Natural Language Processing (NLP) and vision transformers (ViT). Across studies, deep learning models consistently outperformed traditional machine learning techniques in terms of accuracy, sensitivity, and specificity for lung cancer detection in CT scans. This is attributed to the ability of deep learning models to automatically learn discriminative features from medical images and model complex spatial relationships. However, several challenges remain to be addressed before deep learning models can be widely deployed in clinical practice. These include model dependence on training data, generalization across datasets, integration of clinical metadata, and model interpretability. Overall, deep learning demonstrates great potential for lung cancer detection and precision medicine. However, more research is required to rigorously validate models and address risks. This review provides key insights for both computer scientists and clinicians, summarizing progress and future directions for deep learning in medical image analysis.

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Section: Deep Learning Technics Using Proprietary Datasetsmentioning

confidence: 99%

Deep Machine Learning for Medical Diagnosis, Application to Lung Cancer Detection: A Review

Gayap,

Akhloufi

2024

BioMedInformatics

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“…Tan et al created a customised DNN based on the VGG16 architecture in 2022 [7] that uses transfer learning to discriminate between TB lung nodules and early-stage lung malignancies. Using unlocked pretrained weights on CT images from the National Lung Screening Trial and the National Institute of Allergy and Infectious Disease TB Portals, the DNN demonstrated its potential as a dependable, noninvasive screening tool for detecting and distinguishing between LC and tuberculosis, with a detection rate of 90.4% and a F score of 90.1%.In order to classify benign nodules, primary lung cancer, and metastatic lung cancer, Nishio et [9] for the automatic segmentation of lung cancer in [18F] FDG PET/CT scans. The first stage uses a global U-net to extract preliminary tumour regions from the 3D PET/CT volume, and the second stage refines these areas using a localised U-net on chosen slices.…”

Section: Literature Reviewmentioning

confidence: 99%

Convolutional Neural Networks in Malaria Diagnosis: A Study on Cell Image Classification

Ghosh,

Rahat,

Ravindra

et al. 2024

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Malaria, a persistent global health threat caused by Plasmodium parasites, necessitates rapid and accurate identification for effective treatment and containment. This study investigates the utilization of convolutional neural networks (CNNs) to enhance the precision and speed of malaria detection through the classification of cell images infected with malaria. OBJECTIVES: The primary objective of this research is to explore the effectiveness of CNNs in accurately classifying malaria-infected cell images. By employing various deep learning models, including ResNet50, AlexNet, Inception V3, VGG19, VGG16, and MobileNetV2, the study aims to assess the performance of each model and identify their strengths and weaknesses in malaria diagnosis. METHODS: A balanced dataset comprising approximately 8,000 enhanced images of blood cells, evenly distributed between infected and uninfected classes, was utilized for model training and evaluation. Performance evaluation metrics such as precision, recall, F1-score, and accuracy were employed to assess the efficacy of each CNN model in malaria classification. RESULTS: The results demonstrate high accuracy across all models, with AlexNet and VGG19 exhibiting the highest levels of accuracy. However, the selection of a model should consider specific application requirements and constraints, as each model presents unique trade-offs between computational efficiency and performance. CONCLUSION: This study contributes to the burgeoning field of deep learning in healthcare, particularly in utilizing medical imaging for disease diagnosis. The findings underscore the considerable potential of CNNs in enhancing malaria diagnosis. Future research directions may involve further model optimization, exploration of larger and more diverse datasets, and the integration of CNNs into practical diagnostic tools for real-world deployment.

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“…Park et al [11] presented a two-stage U-Net model to boost the segmentation effectiveness of lung tumours by utilizing [18F]FDG PET/CT, as precise segmentation is necessary for determining the functional size of a tumour in this imaging modality. The LifeX program was used to create the tumour volume of interest.…”

Section: Literature Reviewmentioning

confidence: 99%

Hyper-Dense_Lung_Seg: Multimodal-Fusion-Based Modified U-Net for Lung Tumour Segmentation Using Multimodality of CT-PET Scans

Alshmrani,

Ni,

Jiang

et al. 2023

Diagnostics

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The majority of cancer-related deaths globally are due to lung cancer, which also has the second-highest mortality rate. The segmentation of lung tumours, treatment evaluation, and tumour stage classification have become significantly more accessible with the advent of PET/CT scans. With the advent of PET/CT scans, it is possible to obtain both functioning and anatomic data during a single examination. However, integrating images from different modalities can indeed be time-consuming for medical professionals and remains a challenging task. This challenge arises from several factors, including differences in image acquisition techniques, image resolutions, and the inherent variations in the spectral and temporal data captured by different imaging modalities. Artificial Intelligence (AI) methodologies have shown potential in the automation of image integration and segmentation. To address these challenges, multimodal fusion approach-based U-Net architecture (early fusion, late fusion, dense fusion, hyper-dense fusion, and hyper-dense VGG16 U-Net) are proposed for lung tumour segmentation. Dice scores of 73% show that hyper-dense VGG16 U-Net is superior to the other four proposed models. The proposed method can potentially aid medical professionals in detecting lung cancer at an early stage.

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Automatic Lung Cancer Segmentation in [18F]FDG PET/CT Using a Two-Stage Deep Learning Approach

Cited by 20 publications

References 36 publications

Deep Machine Learning for Medical Diagnosis, Application to Lung Cancer Detection: A Review

Deep Machine Learning for Medical Diagnosis, Application to Lung Cancer Detection: A Review

Convolutional Neural Networks in Malaria Diagnosis: A Study on Cell Image Classification

Hyper-Dense_Lung_Seg: Multimodal-Fusion-Based Modified U-Net for Lung Tumour Segmentation Using Multimodality of CT-PET Scans

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