A Review on Lung and Nodule Segmentation Techniques

Kamble, Bhawana; Sahu, Satya Prakash; Doriya, Rajesh

doi:10.1007/978-981-15-0694-9_52

“…CT imaging is a popular technique for the diagnosis of lung diseases [23], [24]. In practice, segmenting different organs and lesions from chest CT slices can provide crucial information for doctors to diagnose and quantify lung diseases [25], [26].…”

Section: A Segmentation In Chest Ctmentioning

confidence: 99%

Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images

Fan

¹

,

Zhou

²

,

Ji

³

et al. 2020

IEEE Trans. Med. Imaging

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Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to face an existential health crisis. Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. However, segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues. Further, collecting a large amount of data is impractical within a short time period, inhibiting the training of a deep model. To address these challenges, a novel COVID-19 Lung Infection Segmentation Deep Network (Inf-Net) is proposed to automatically identify infected regions from chest CT slices. In our Inf-Net, a parallel partial decoder is used to aggregate the high-level features and generate a global map. Then, the implicit reverse attention and explicit edge-attention are utilized to model the boundaries and enhance the representations. Moreover, to alleviate the shortage of labeled data, we present a semi-supervised segmentation framework based on a randomly selected propagation strategy, which only requires a few labeled images and leverages primarily unlabeled data. Our semi-supervised framework can improve the learning ability and achieve a higher performance. Extensive experiments on our COVID-SemiSeg and real CT volumes demonstrate that the proposed Inf-Net outperforms most cutting-edge segmentation models and advances the state-of-the-art performance.

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“…CT imaging is a popular technique for the diagnosis of lung diseases [23], [24]. In practice, segmenting different organs and lesions from chest CT slices can provide crucial information for doctors to diagnose and quantify lung diseases [25].…”

Section: A Segmentation In Chest Ctmentioning

confidence: 99%

Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images

Fan

¹

,

Zhou

²

,

Ji

³

et al. 2020

Preprint

View full text Add to dashboard Cite

Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to face an existential health crisis. Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. However, segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues. Further, collecting a large amount of data is impractical within a short time period, inhibiting the training of a deep model. To address these challenges, a novel COVID-19 Lung Infection Segmentation Deep Network (Inf-Net) is proposed to automatically identify infected regions from chest CT slices. In our Inf-Net, a parallel partial decoder is used to aggregate the high-level features and generate a global map. Then, the implicit reverse attention and explicit edge-attention are utilized to model the boundaries and enhance the representations. Moreover, to alleviate the shortage of labeled data, we present a semi-supervised segmentation framework based on a randomly selected propagation strategy, which only requires a few labeled images and leverages primarily unlabeled data. Our semi-supervised framework can improve the learning ability and achieve a higher performance. Extensive experiments on our COVID-SemiSeg and real CT volumes demonstrate that the proposed Inf-Net outperforms most cutting-edge segmentation models and advances the state-of-theart performance.

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“… 2006 ; Kamble et al. 2020 ). Radiologists segmented the lung lesion area by viewing the lung CT images and combining them with clinical information to diagnose COVID-19 patients.…”

Section: Related Workmentioning

confidence: 99%

DBF-Net: a semi-supervised dual-task balanced fusion network for segmenting infected regions from lung CT images

Zhang

¹

,

Xu

²

,

Yuan

³

2022

Evolving Systems

0

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Accurate segmentation of infected regions in lung computed tomography (CT) images is essential to improve the timeliness and effectiveness of treatment for coronavirus disease 2019 (COVID-19). However, the main difficulties in developing of lung lesion segmentation in COVID-19 are still the fuzzy boundary of the lung-infected region, the low contrast between the infected region and the normal trend region, and the difficulty in obtaining labeled data. To this end, we propose a novel dual-task consistent network framework that uses multiple inputs to continuously learn and extract lung infection region features, which is used to generate reliable label images (pseudo-labels) and expand the dataset. Specifically, we periodically feed multiple sets of raw and data-enhanced images into two trunk branches of the network; the characteristics of the lung infection region are extracted by a lightweight double convolution (LDC) module and fusiform equilibrium fusion pyramid (FEFP) convolution in the backbone. According to the learned features, the infected regions are segmented, and pseudo-labels are made based on the semi-supervised learning strategy, which effectively alleviates the semi-supervised problem of unlabeled data. Our proposed semi-supervised dual-task balanced fusion network (DBF-Net) creates pseudo-labels on the COVID-SemiSeg dataset and the COVID-19 CT segmentation dataset. Furthermore, we perform lung infection segmentation on the DBF-Net model, with a segmentation sensitivity of 70.6% and specificity of 92.8%. The results of the investigation indicate that the proposed network greatly enhances the segmentation ability of COVID-19 infection.

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A Review on Lung and Nodule Segmentation Techniques

Cited by 26 publications

References 30 publications

Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images

Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images

Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images

DBF-Net: a semi-supervised dual-task balanced fusion network for segmenting infected regions from lung CT images

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