Lung Field Segmentation in Chest Radiographs From Boundary Maps by a Structured Edge Detector

Yang, Wei; Liu, Yunbi; Lin, Liyan; Yun, Zhaoqiang; Lu, Zhentai; Feng, Qianjin; Chen, Wufan

doi:10.1109/jbhi.2017.2687939

Cited by 55 publications

(31 citation statements)

References 28 publications

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“…Applying additional minor post-processing resulted in further decrease of the MACD measure with cleaner and more precise segmentations for all three structures as displayed in Figure 3. Table 3 presents the final comparison between our top selected model, the multi-class U-Net VGG16 with dice loss, to state-of-the-art methods [6,4,3,2,5] and human observer segmentations [6]. Our model outperformed all state-ofthe-art methods tested in this study and the human observer for the lungs and heart segmentation.…”

Section: Resultsmentioning

confidence: 94%

Improving the Segmentation of Anatomical Structures in Chest Radiographs Using U-Net with an ImageNet Pre-trained Encoder

Frid-Adar¹,

Ben-Cohen²,

Amer³

et al. 2018

Lecture Notes in Computer Science

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Accurate segmentation of anatomical structures in chest radiographs is essential for many computer-aided diagnosis tasks. In this paper we investigate the latest fully-convolutional architectures for the task of multi-class segmentation of the lungs field, heart and clavicles in a chest radiograph. In addition, we explore the influence of using different loss functions in the training process of a neural network for semantic segmentation. We evaluate all models on a common benchmark of 247 X-ray images from the JSRT database and ground-truth segmentation masks from the SCR dataset. Our best performing architecture, is a modified U-Net that benefits from pre-trained encoder weights. This model outperformed the current state-of-the-art methods tested on the same benchmark, with Jaccard overlap scores of 96.1% for lung fields, 90.6% for heart and 85.5% for clavicles.

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Section: Resultsmentioning

confidence: 94%

Improving the Segmentation of Anatomical Structures in Chest Radiographs Using U-Net with an ImageNet Pre-trained Encoder

Frid-Adar¹,

Ben-Cohen²,

Amer³

et al. 2018

Lecture Notes in Computer Science

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“…Classic-based approaches, including rule-based methods [4][5][6], pixel classification-based methods [7], [8], deformable shape-based methods [9][10][11], and hybrid methods [12][13][14][15], have different focuses and advantages. Rule-based methods [4][5][6], which use predefined knowledge about the lung field to create a set of rules, are usually used as initial segmentation algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…Typically, compared to the ASM and AAM, scale-invariant feature transforms [11], shape particle filtering [16], and global edge and region forces [19] have demonstrated superior performance through extracting low level localized appearance features and high level global features. To further improve the performance of lung field segmentation, hybrid methods [12][13][14][15] combine the best parts of pixel classification and deformable shape to refine the detection of the lung boundary. Peng et al [20] proposed a hybrid semi-automatic method called Hull-Closed Polygonal Line Method (Hull-CPLM) to detect the boundaries of the lung region of interest.…”

Section: Introductionmentioning

confidence: 99%

View Identification Assisted Fully Convolutional Network for Lung Field Segmentation of Frontal and Lateral Chest Radiographs

Zhong

Xie

et al. 2021

IEEE Access

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Locating lung field is a critical and fundamental processing stage in the automated analysis of chest radiographs (CXRs) for pulmonary disorders. During the routine examination of CXRs, using both frontal and lateral CXRs can benefit clinical diagnosis of cardiothoracic and lung diseases. However, the accurate segmentation of lung fields on both frontal and lateral CXRs is still challenging due to the blurry boundary of the lung field on lateral CXRs and the poor generalization ability of the models. Existing deep learning-based methods focused on lung field segmentation on frontal CXRs, and the generalization ability of these methods on the different type of CXRs (e.g., pediatric CXRs) and new lung diseases (e.g., COVID-19) has not been tested. In this paper, a view identification assisted fully convolutional network (VI-FCN) is proposed for the segmentation of lung fields on frontal and lateral CXRs simultaneously. The VI-FCN consists of an FCN branch for lung field segmentation and a view identification branch for identification of the frontal and lateral CXRs and for enhancing the lung field segmentation. To improve the generalization ability of VI-FCN, six public datasets and our frontal and lateral CXRs (over 2000 CXRs) were collected for training. The segmentation of lung fields on the Japanese Society of Radiological Technology (JSRT) dataset yields mean dice similarity coefficient (DSC) of 0.979 ± 0.008, mean Jaccard index (Ω) of 0.959 ± 0.016, and mean boundary distance (MBD) of 1.023 ± 0.487mm. Besides, the VI-FCN achieves mean DSC of 0.973 ± 0.010, mean Ω of 0.947 ± 0.018, and mean MBD of 1.923 ± 0.755mm for the segmentation of lung fields on our lateral CXRs. The experiments demonstrate the superior performance of the proposed VI-FCN over most of existing state-of-the-art methods. Moreover, the proposed VI-FCN achieves promising results on untrained pediatric CXRs and COVID-19 datasets.INDEX TERMS Chest radiographs, Lung field segmentation, Generalization ability, COVID-19.YUHUA XI received the Bachelor's degree in Biomedical Engineering from the Southern Medical University, Guangzhou, China, in 2018. She is currently pursuing the master of engineering degree with the Department of Biomedical Engineering, the Southern Medical University. Her research focuses on the segmentation of medical images and bone suppression in CXRs.

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“…Yu et al used the DP algorithm to effectively trace the optimal seam line between two images [11]. Yang used the DP algorithm to quickly extract the contours of the ribcage field in chest radiographs [12]. Following these studies, we apply the DP algorithm to trace the contours of UR from four seed points in two cost maps.…”

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation of Ulna and Radius in Forearm Radiographs

Gou

Rao²,

Feng

et al. 2019

Computational and Mathematical Methods in Medicine

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Automatic segmentation of ulna and radius (UR) in forearm radiographs is a necessary step for single X-ray absorptiometry bone mineral density measurement and diagnosis of osteoporosis. Accurate and robust segmentation of UR is difficult, given the variation in forearms between patients and the nonuniformity intensity in forearm radiographs. In this work, we proposed a practical automatic UR segmentation method through the dynamic programming (DP) algorithm to trace UR contours. Four seed points along four UR diaphysis edges are automatically located in the preprocessed radiographs. Then, the minimum cost paths in a cost map are traced from the seed points through the DP algorithm as UR edges and are merged as the UR contours. The proposed method is quantitatively evaluated using 37 forearm radiographs with manual segmentation results, including 22 normal-exposure and 15 low-exposure radiographs. The average Dice similarity coefficient of our method reached 0.945. The average mean absolute distance between the contours extracted by our method and a radiologist is only 5.04 pixels. The segmentation performance of our method between the normal- and low-exposure radiographs was insignificantly different. Our method was also validated on 105 forearm radiographs acquired under various imaging conditions from several hospitals. The results demonstrated that our method was fairly robust for forearm radiographs of various qualities.

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Lung Field Segmentation in Chest Radiographs From Boundary Maps by a Structured Edge Detector

Cited by 55 publications

References 28 publications

Improving the Segmentation of Anatomical Structures in Chest Radiographs Using U-Net with an ImageNet Pre-trained Encoder

Improving the Segmentation of Anatomical Structures in Chest Radiographs Using U-Net with an ImageNet Pre-trained Encoder

View Identification Assisted Fully Convolutional Network for Lung Field Segmentation of Frontal and Lateral Chest Radiographs

Automatic Segmentation of Ulna and Radius in Forearm Radiographs

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