Automatic Lung Segmentation on Chest X-rays Using Self-Attention Deep Neural Network

Kim, Minki; Lee, Byoung-Dai

doi:10.3390/s21020369

Cited by 60 publications

(42 citation statements)

References 17 publications

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“…The proposed model has trained on JSRT and MC dataset and tested it on NIH dataset [ 164 ]. Kim et al [ 85 ] proposed the self-attention module to capture the global features in input X-ray images. The proposed attention module is applied to the UNet for lung segmentation.…”

Section: Segmentationmentioning

confidence: 99%

Segmentation and classification on chest radiography: a systematic survey

Agrawal

Choudhary

2022

Vis Comput

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Chest radiography (X-ray) is the most common diagnostic method for pulmonary disorders. A trained radiologist is required for interpreting the radiographs. But sometimes, even experienced radiologists can misinterpret the findings. This leads to the need for computer-aided detection diagnosis. For decades, researchers were automatically detecting pulmonary disorders using the traditional computer vision (CV) methods. Now the availability of large annotated datasets and computing hardware has made it possible for deep learning to dominate the area. It is now the modus operandi for feature extraction, segmentation, detection, and classification tasks in medical imaging analysis. This paper focuses on the research conducted using chest X-rays for the lung segmentation and detection/classification of pulmonary disorders on publicly available datasets. The studies performed using the Generative Adversarial Network (GAN) models for segmentation and classification on chest X-rays are also included in this study. GAN has gained the interest of the CV community as it can help with medical data scarcity. In this study, we have also included the research conducted before the popularity of deep learning models to have a clear picture of the field. Many surveys have been published, but none of them is dedicated to chest X-rays. This study will help the readers to know about the existing techniques, approaches, and their significance.

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Section: Segmentationmentioning

confidence: 99%

Segmentation and classification on chest radiography: a systematic survey

Agrawal

Choudhary

2022

Vis Comput

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“…Four network branches with independent parameters then further learn higher-level global and local features from previous organ-related features. Based on the observation that organs can be precisely localized by organ masks, we adopt anatomical segmentation techniques [ 21 , 44 ] to first automatically generate organ masks and then use the generated organ masks to constrain the attention learning of the proposed MA. In particular, we employ an off-the-shelf segmentation method [ 21 ] to generate four organ masks, the all-organ mask

, the left-lung mask

, the right-lung mask

, and the heart mask

as illustrated in Figure 4 .…”

Section: Methodsmentioning

confidence: 99%

Part-Aware Mask-Guided Attention for Thorax Disease Classification

Zhang

Yang

Luo

et al. 2021

Entropy

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Thorax disease classification is a challenging task due to complex pathologies and subtle texture changes, etc. It has been extensively studied for years largely because of its wide application in computer-aided diagnosis. Most existing methods directly learn global feature representations from whole Chest X-ray (CXR) images, without considering in depth the richer visual cues lying around informative local regions. Thus, these methods often produce sub-optimal thorax disease classification performance because they ignore the very informative pathological changes around organs. In this paper, we propose a novel Part-Aware Mask-Guided Attention Network (PMGAN) that learns complementary global and local feature representations from all-organ region and multiple single-organ regions simultaneously for thorax disease classification. Specifically, multiple innovative soft attention modules are designed to progressively guide feature learning toward the global informative regions of whole CXR image. A mask-guided attention module is designed to further search for informative regions and visual cues within the all-organ or single-organ images, where attention is elegantly regularized by automatically generated organ masks and without introducing computation during the inference stage. In addition, a multi-task learning strategy is designed, which effectively maximizes the learning of complementary local and global representations. The proposed PMGAN has been evaluated on the ChestX-ray14 dataset and the experimental results demonstrate its superior thorax disease classification performance against the state-of-the-art methods.

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“…However, in the past decade, deep neural networks seem to have been given more attention as the preferred method when doing lung segmentation of digital xrays: In [17], two deep convolutional neural network models were used, separating the tasks in four steps main steps: (1) image acquisition, (2) initial segmentation, (3) reconstruction and (4) final segmentation. Numerous other works tend to use a type of U-Net architecture based neural network, as in [18] with a variational encoder and decoder, or with attention modules such as in [19] or [20].…”

Section: Related Workmentioning

confidence: 99%

“…The data for all three datasets were split randomly, with 70% of the data reserved for training, 10% for validation, and 20% for testing. This specific data split was done in order to compare our proposed approach to other high-performing models ( [30], [20], and [19]) that split these same datasets in similar fashion.…”

Section: A Datasetsmentioning

confidence: 99%

Development of a lung segmentation algorithm for analog imaged chest X-Ray: preliminary results

Renzo¹,

Fernandez²,

Baceti³

et al. 2021

Anais Do 15. Congresso Brasileiro De Inteligência Computacional

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Analog X-Ray radiography is still used in many underdeveloped regions around the world. To allow these populations to benefit from advances in automatic computer-aided detection (CAD) systems, X-Ray films must be digitized. Unfortunately, this procedure may introduce artefacts which may severely impair the performance of such systems. This work investigates the impact digitized images may cause to deep neural networks trained for lung (semantic) segmentation on digital x-ray samples. While three public datasets for lung segmentation evaluation exist for digital samples, none are available for digitized data. To this end, a U-Net architecture was trained on publicly available data, and used to predict lung segmentation on a newly annotated set of digitized images. Our results show that the model is capable to effectively identify lung segmentation at digital X-Rays with a high intra-dataset (PR AUC: 0.99) and cross-dataset (PR AUC: 0.99) performances on unseen test data. When challenged against analog imaged films, the performance is substantially degraded (PR AUC: 0.90). Our analysis further suggests that the use of maximum F1 and precision-recall AUC (PR AUC) measures are not informative to identify segmentation problems in images.

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Automatic Lung Segmentation on Chest X-rays Using Self-Attention Deep Neural Network

Cited by 60 publications

References 17 publications

Segmentation and classification on chest radiography: a systematic survey

Segmentation and classification on chest radiography: a systematic survey

Part-Aware Mask-Guided Attention for Thorax Disease Classification

Development of a lung segmentation algorithm for analog imaged chest X-Ray: preliminary results

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