Revisiting segmentation of lung tumors from CT images

Farheen, Farhanaz; Shamil, Md. Salman; Ibtehaz, Nabil; Rahman, M. Sohel

doi:10.1016/j.compbiomed.2022.105385

Cited by 21 publications

(7 citation statements)

References 12 publications

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“…In a study, augmentation with elastic deformations, which add shape variations, brings noise and damage when the deformation field is varied seriously (Müller et al 2021 ). In a different approach, as it is identified by the authors, the predicted tumor regions are prone to imperfections (Farheen et al 2022 ).…”

Section: Discussionmentioning

confidence: 99%

Medical image data augmentation: techniques, comparisons and interpretations

Göçeri

2023

Artif Intell Rev

114

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Designing deep learning based methods with medical images has always been an attractive area of research to assist clinicians in rapid examination and accurate diagnosis. Those methods need a large number of datasets including all variations in their training stages. On the other hand, medical images are always scarce due to several reasons, such as not enough patients for some diseases, patients do not want to allow their images to be used, lack of medical equipment or equipment, inability to obtain images that meet the desired criteria. This issue leads to bias in datasets, overfitting, and inaccurate results. Data augmentation is a common solution to overcome this issue and various augmentation techniques have been applied to different types of images in the literature. However, it is not clear which data augmentation technique provides more efficient results for which image type since different diseases are handled, different network architectures are used, and these architectures are trained and tested with different numbers of data sets in the literature. Therefore, in this work, the augmentation techniques used to improve performances of deep learning based diagnosis of the diseases in different organs (brain, lung, breast, and eye) from different imaging modalities (MR, CT, mammography, and fundoscopy) have been examined. Also, the most commonly used augmentation methods have been implemented, and their effectiveness in classifications with a deep network has been discussed based on quantitative performance evaluations. Experiments indicated that augmentation techniques should be chosen carefully according to image types.

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Section: Discussionmentioning

confidence: 99%

Medical image data augmentation: techniques, comparisons and interpretations

Göçeri

2023

Artif Intell Rev

114

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“…The patient airway cross‐sectional area was calculated using the transverse image at the distal end of the tracheal catheter in the CT images. All calculations were performed in the hospital PACS system (Figure 1 ) (Farheen et al., 2022 ; Song, 2021 ; Wu et al., 2021 ).…”

Section: Methodsmentioning

confidence: 99%

Risk factor evaluation of cuff pressure of >30 cmH₂O to stop air leakage during mechanical ventilation: A prospective observational study

Wu,

Shen

et al. 2024

Nursing Open

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AimThe commonly recommended endotracheal tube cuff pressure is 20–30 cmH2O. However, some patients require a cuff pressure of >30 cmH2O to prevent air leakage. The study aims to determine the risk factors that contribute to the endotracheal tube cuff pressure of >30 cmH2O to prevent air leakage.DesignA multi‐centre prospective observational study.MethodsEligible patients undergoing mechanical ventilation in the intensive care unit of three hospitals between March 2020 and July 2022 were included. The endotracheal tube cuff pressure to prevent air leakage was determined using the minimal occlusive volume technique. The patient demographics and clinical information were collected.ResultsA total of 284 patients were included. Among these patients, 55 (19.37%) patients required a cuff pressure of >30 cmH2O to prevent air leakage. The multivariate logistic regression results revealed that the surgical operation (odds ratio [OR]: 8.485, 95% confidence interval [CI]: 1.066–67.525, p = 0.043) was inversely associated with the endotracheal tube cuff pressure of >30 cmH2O, while the oral intubation route (OR: 0.127, 95% CI: 0.022–0.750, p = 0.023) and cuff inner diameter minus tracheal area (OR: 0.949, 95% CI: 0.933–0.966, p < 0.001) were negatively associated with the endotracheal tube cuff pressure of >30 cmH2O. Therefore, a significant number of patients require an endotracheal tube cuff pressure of ＞30 cmH2O to prevent air leakage. Several factors, including the surgical operation, intubation route, and difference between the cuff inner diameter and tracheal area at the T3 vertebra, should be considered when determining the appropriate cuff pressure during mechanical ventilation.

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“…Why there hasn't been enough study on how to discover lung cancer nodules that have been divided into segments is explained in Table 1. Researchers from [11][12][13][14] used the identical data set, but the model's robustness was degraded. Because the U-NET could not be utilized with new data types, the IOU intersection and dice coefficient index accuracy were unavailable.…”

Section: Related Workmentioning

confidence: 99%

Lung Cancer Segmentation with Three-Parameter Logistic Type Distribution燤odel

Bhattacharyya¹,

Joshua²,

Rao³

et al. 2023

Computers, Materials &Amp; Continua

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Lung cancer is the leading cause of mortality in the world affecting both men and women equally. When a radiologist just focuses on the patient's body, it increases the amount of strain on the radiologist and the likelihood of missing pathological information such as abnormalities are increased. One of the primary objectives of this research work is to develop computerassisted diagnosis and detection of lung cancer. It also intends to make it easier for radiologists to identify and diagnose lung cancer accurately. The proposed strategy which was based on a unique image feature, took into consideration the spatial interaction of voxels that were next to one another. Using the U-NET+Three parameter logistic distribution-based technique, we were able to replicate the situation. The proposed technique had an average Dice co-efficient (DSC) of 97.3%, a sensitivity of 96.5% and a specificity of 94.1% when tested on the Luna-16 dataset. This research investigates how diverse lung segmentation, juxta pleural nodule inclusion, and pulmonary nodule segmentation approaches may be applied to create Computer Aided Diagnosis (CAD) systems. When we compared our approach to four other lung segmentation methods, we discovered that ours was the most successful. We employed 40 patients from Luna-16 datasets to evaluate this. In terms of DSC performance, the findings demonstrate that the suggested technique outperforms the other strategies by a significant margin.

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Revisiting segmentation of lung tumors from CT images

Cited by 21 publications

References 12 publications

Medical image data augmentation: techniques, comparisons and interpretations

Medical image data augmentation: techniques, comparisons and interpretations

Risk factor evaluation of cuff pressure of >30 cmH₂O to stop air leakage during mechanical ventilation: A prospective observational study

Lung Cancer Segmentation with Three-Parameter Logistic Type Distribution燤odel

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Revisiting segmentation of lung tumors from CT images

Cited by 21 publications

References 12 publications

Medical image data augmentation: techniques, comparisons and interpretations

Medical image data augmentation: techniques, comparisons and interpretations

Risk factor evaluation of cuff pressure of >30 cmH2O to stop air leakage during mechanical ventilation: A prospective observational study

Lung Cancer Segmentation with Three-Parameter Logistic Type Distribution燤odel

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Product

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Risk factor evaluation of cuff pressure of >30 cmH₂O to stop air leakage during mechanical ventilation: A prospective observational study