Deep Learning for Pulmonary Image Analysis: Classification, Detection, and Segmentation

Shoji, K.; Hirano, Yasushi; Mabu, Shingo

doi:10.1007/978-3-030-33128-3_3

Cited by 16 publications

(17 citation statements)

References 9 publications

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“…They deduced that this method is highly cost-effective with good accuracy of about 76·4%, almost equivalent to human accuracy [ 15 ]. Kido et al used algorithms like fully convolutional network (FCN), Lung nodule, R-CNN, Residual U-Net, U-Net, and V-Net and deduced that using DL, computer-aided diagnosis, is going to be much easier and more accurate than even an experienced radiologist; not just IPF, various other lung abnormalities can be detected using DL [ 5 ].…”

Section: Related Workmentioning

confidence: 99%

“…When scarring occurs, the patient finds it difficult to breathe normally, which eventually leads to shortness of breath even when the person is not performing any strenuous exercise [ 4 ]. Patients with this disease display fibrotic sections, honeycombing, and wide-ranging patchy ground-glass areas with or without consolidations, depicting the presence of pleural fluid within the CT scans [ 5 ]. Hence, biomedical imageries are a massive source of knowledge beneficial to feed analytical tools within revealing pathologies [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

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FVC-NET: An Automated Diagnosis of Pulmonary Fibrosis Progression Prediction Using Honeycombing and Deep Learning

Yadav

Saxena

Kumar

et al. 2022

Computational Intelligence and Neuroscience

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Pulmonary fibrosis is a severe chronic lung disease that causes irreversible scarring in the tissues of the lungs, which results in the loss of lung capacity. The Forced Vital Capacity (FVC) of the patient is an interesting measure to investigate this disease to have the prognosis of the disease. This paper proposes a deep learning-based FVC-Net architecture to predict the progression of the disease from the patient’s computed tomography (CT) scan and the patient’s metadata. The input to the model combines the image score generated based on the degree of honeycombing for a patient identified based on segmented lung images and the metadata. This input is then fed to a 3-layer net to obtain the final output. The performance of the proposed FVC-Net model is compared with various contemporary state-of-the-art deep learning-based models, which are available on a cohort from the pulmonary fibrosis progression dataset. The model showcased significant improvement in the performance over other models for modified Laplace Log-Likelihood (−6.64). Finally, the paper concludes with some prospects to be explored in the proposed study.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

FVC-NET: An Automated Diagnosis of Pulmonary Fibrosis Progression Prediction Using Honeycombing and Deep Learning

Yadav

Saxena

Kumar

et al. 2022

Computational Intelligence and Neuroscience

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“…For the lung segmentation task, several deep learning-based methods have been proposed (e.g., [ 6 , 11 , 24 , 25 , 26 ]). The interested reader is encouraged to refer to comprehensive reviews [ 27 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

Lung Segmentation on High-Resolution Computerized Tomography Images Using Deep Learning: A Preliminary Step for Radiomics Studies

et al. 2020

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Background: The aim of this work is to identify an automatic, accurate, and fast deep learning segmentation approach, applied to the parenchyma, using a very small dataset of high-resolution computed tomography images of patients with idiopathic pulmonary fibrosis. In this way, we aim to enhance the methodology performed by healthcare operators in radiomics studies where operator-independent segmentation methods must be used to correctly identify the target and, consequently, the texture-based prediction model. Methods: Two deep learning models were investigated: (i) U-Net, already used in many biomedical image segmentation tasks, and (ii) E-Net, used for image segmentation tasks in self-driving cars, where hardware availability is limited and accurate segmentation is critical for user safety. Our small image dataset is composed of 42 studies of patients with idiopathic pulmonary fibrosis, of which only 32 were used for the training phase. We compared the performance of the two models in terms of the similarity of their segmentation outcome with the gold standard and in terms of their resources’ requirements. Results: E-Net can be used to obtain accurate (dice similarity coefficient = 95.90%), fast (20.32 s), and clinically acceptable segmentation of the lung region. Conclusions: We demonstrated that deep learning models can be efficiently applied to rapidly segment and quantify the parenchyma of patients with pulmonary fibrosis, without any radiologist supervision, in order to produce user-independent results.

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“…For such segmentation models, deep learning methods have predominantly taken over, often based on convolutional neural networks (CNNs). For image processing, such CNNs have been shown to overperform humans in classification and detection methods (e.g., [ 36 , 37 ]). They are increasingly being used for tasks like multi-organ segmentation [ 38 ].…”

Section: Preoperative Planning and Navigationmentioning

confidence: 99%

How molecular imaging will enable robotic precision surgery

Wendler

Leeuwen

Navab

et al. 2021

Eur J Nucl Med Mol Imaging

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Molecular imaging is one of the pillars of precision surgery. Its applications range from early diagnostics to therapy planning, execution, and the accurate assessment of outcomes. In particular, molecular imaging solutions are in high demand in minimally invasive surgical strategies, such as the substantially increasing field of robotic surgery. This review aims at connecting the molecular imaging and nuclear medicine community to the rapidly expanding armory of surgical medical devices. Such devices entail technologies ranging from artificial intelligence and computer-aided visualization technologies (software) to innovative molecular imaging modalities and surgical navigation (hardware). We discuss technologies based on their role at different steps of the surgical workflow, i.e., from surgical decision and planning, over to target localization and excision guidance, all the way to (back table) surgical verification. This provides a glimpse of how innovations from the technology fields can realize an exciting future for the molecular imaging and surgery communities.

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Deep Learning for Pulmonary Image Analysis: Classification, Detection, and Segmentation

Cited by 16 publications

References 9 publications

FVC-NET: An Automated Diagnosis of Pulmonary Fibrosis Progression Prediction Using Honeycombing and Deep Learning

FVC-NET: An Automated Diagnosis of Pulmonary Fibrosis Progression Prediction Using Honeycombing and Deep Learning

Lung Segmentation on High-Resolution Computerized Tomography Images Using Deep Learning: A Preliminary Step for Radiomics Studies

How molecular imaging will enable robotic precision surgery

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