Pneumothorax Segmentation with Effective Conditioned Post-Processing in Chest X-Ray

Groza, Vladimir; Kuzin, A. Yu.

doi:10.1109/isbiworkshops50223.2020.9153444

Cited by 10 publications

(12 citation statements)

References 8 publications

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“…• The performance of deep learning models can further be improved by ensemble modelling in which different models are trained on the training set and final results are combined via voting or averaging methods. The same is evident from [73], [74]. However, ensemble models face the problem of time and computational complexity.…”

Section: A Comparative Analysismentioning

confidence: 67%

“…The solutions mostly adapted for solving class imbalance can be categorized as data-level, algorithm-level and hybrid approaches [70]. Data-level approaches solve the classimbalance problem by modifying the class distribution in a dataset [40], [71]- [73]. In algorithm level approaches, the class-imbalance problem is solved by altering the classifier algorithm [20], [74]- [76].…”

Section: Data Balancingmentioning

confidence: 99%

“…The proposed method consisted of three consecutive steps. A major contribution in the field of extraction of the area of pneumothorax was made in [73]. Here an ensemble of three LinkNet networks [111] In [60], a segmentation model was proposed for the precise prediction of the portion of lung affected by pneumothorax.…”

Section: • Localizationmentioning

confidence: 99%

“…On the other hand, DL algorithms do the job of feature extraction by itself. Despite the several limitations of DL technique such as requirement of huge amount of training data, computational complexity, extensive resource consumption and optimizing the hyperparameters, mostly researchers have adapted DL techniques because of their outstanding performance [22], [43], [61], [73], [110], [116]. • The performance of deep learning models can further be improved by ensemble modelling in which different models are trained on the training set and final results are combined via voting or averaging methods.…”

Section: A Comparative Analysismentioning

confidence: 99%

See 3 more Smart Citations

Automatic Diagnosis of Pneumothorax From Chest Radiographs: A Systematic Literature Review

Iqbal

Shaukat²,

Akram

et al. 2021

IEEE Access

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Among various medical imaging tools, chest radiographs are the most important and widely used diagnostic tool for the detection of thoracic pathologies. Remarkable research is being carried out to propose robust automatic diagnostic tools for the detection of pathologies from the chest radiographs. Artificial Intelligence techniques have been found to give promising results in automating the field of medicine. Lot of research has been done for automatic detection of pneumothorax from chest radiographs while proposing several frameworks based on artificial intelligence techniques. Undoubtedly, several models are available for automatic diagnosis of pneumothorax, however a summarized review of the existing literature is still missing. This study summarizes the existing literature for pneumothorax detection from chest x-rays along with describing the available chest radiographs datasets. It will help the researchers to select the optimal and most effective model with respect to the real-time scenarios. The comparative analysis of the literature is provided in terms of goodness, usability and quality along with highlighting the research gaps for further investigation. From the literature, it is evident that pneumothorax is more common in men as compared to women. Additionally, the proposed models have achieved incredible results for pneumothorax detection on selected datasets, however, the effectiveness of proposed models in real-time cases cannot be claimed, as none of the models have been implemented clinically yet. This issue can be solved by external validation of the models. Furthermore, the class-imbalance problem in most of the medical image dataset has been solved by algorithm-level-techniques. Moreover, there is need to put more effort in combined detection and localization of pneumothorax as mostly research is limited to either classification or localization of pathology. So far, best results have been achieved by deep-learning based models with Area-underreceiver-operating-characteristic-curve (AUC) of 88.87% for classification, and Dice-similarity-coefficient (DSC) of 88.21% for localization of pneumothorax. Thus, the outstanding abilities of deep learning techniques can be deployed for developing robust models for pneumothorax detection.

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Section: A Comparative Analysismentioning

confidence: 67%

Section: Data Balancingmentioning

confidence: 99%

Section: • Localizationmentioning

confidence: 99%

Section: A Comparative Analysismentioning

confidence: 99%

See 2 more Smart Citations

Automatic Diagnosis of Pneumothorax From Chest Radiographs: A Systematic Literature Review

Iqbal

Shaukat²,

Akram

et al. 2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Feng et al [36] retained the largest interconnected block and removed other parts in the prediction mask to identify an organ to perform multi-organ segmentation from CT images. Groza and Kuzin [37] regraded small blocks in the prediction mask as noise to improve pneumothorax segmentation from chest X-ray images. Birenbaum and Greenspan [38] set a threshold of lesion size to perform multiple sclerosis lesion segmentation using MRI images.…”

Section: Related Work a Post-processing Methods For Image Segmentationmentioning

confidence: 99%

Model-Agnostic Post-Processing Based on Recursive Feedback for Medical Image Segmentation

Kim

Kang²

2021

IEEE Access

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Brain Tumor Segmentation and Associated Uncertainty Evaluation Using Multi-sequences MRI Mixture Data Preprocessing

Groza¹,

Tuchinov

Amelina

et al. 2021

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

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Pneumothorax Segmentation with Effective Conditioned Post-Processing in Chest X-Ray

Cited by 10 publications

References 8 publications

Automatic Diagnosis of Pneumothorax From Chest Radiographs: A Systematic Literature Review

Automatic Diagnosis of Pneumothorax From Chest Radiographs: A Systematic Literature Review

Model-Agnostic Post-Processing Based on Recursive Feedback for Medical Image Segmentation

Brain Tumor Segmentation and Associated Uncertainty Evaluation Using Multi-sequences MRI Mixture Data Preprocessing

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