Automatic Pathological Lung Segmentation in Low-Dose CT Image Using Eigenspace Sparse Shape Composition

Chen, Geng; Xiang, Dehui; Zhang, Bin; Tian, Haihong; Yang, Xiaoling; Shi, Fei; Zhu, Weifang; Tian, Bei; Chen, Xinjian

doi:10.1109/tmi.2018.2890510

Cited by 32 publications

(14 citation statements)

References 34 publications

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“…However, several methods do not separate all three objects, other methods rely on image properties and relative shape‐ and intensity‐based characteristics that may change considerably in abnormal cases, and many approaches are validated on a relatively low number of images . The segmentation of anomalous images, as we address here, is certainly an open problem and this can be easily verified in recent works based on deep learning . The aforementioned approaches, nonetheless, still present a few limitations and need further investigation.…”

Section: Introductionmentioning

confidence: 96%

ALTIS: A fast and automatic lung and trachea CT‐image segmentation method

et al. 2019

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Purpose: The automated segmentation of each lung and trachea in CT scans is commonly taken as a solved problem. Indeed, existing approaches may easily fail in the presence of some abnormalities caused by a disease, trauma, or previous surgery. For robustness, we present ALTIS (implementation is available at http://lids.ic.unicamp.br/downloads)a fast automatic lung and trachea CT-image segmentation method that relies on image features and relative shape-and intensity-based characteristics less affected by most appearance variations of abnormal lungs and trachea. Methods: ALTIS consists of a sequence of image foresting transforms (IFTs) organized in three main steps: (a) lung-and-trachea extraction, (b) seed estimation inside background, trachea, left lung, and right lung, and (c) their delineation such that each object is defined by an optimum-path forest rooted at its internal seeds. We compare ALTIS with two methods based on shape models (SOSM-S and MALF), and one algorithm based on seeded region growing (PTK). Results:The experiments involve the highest number of scans found in literature -1255 scans, from multiple public data sets containing many anomalous cases, being only 50 normal scans used for training and 1205 scans used for testing the methods. Quantitative experiments are based on two metrics, DICE and ASSD. Furthermore, we also demonstrate the robustness of ALTIS in seed estimation. Considering the test set, the proposed method achieves an average DICE of 0.987 for both lungs and 0.898 for the trachea, whereas an average ASSD of 0.938 for the right lung, 0.856 for the left lung, and 1.316 for the trachea. These results indicate that ALTIS is statistically more accurate and considerably faster than the compared methods, being able to complete segmentation in a few seconds on modern PCs. Conclusion: ALTIS is the most effective and efficient choice among the compared methods to segment left lung, right lung, and trachea in anomalous CT scans for subsequent detection, segmentation, and quantitative analysis of abnormal structures in the lung parenchyma and pleural space.

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Section: Introductionmentioning

confidence: 96%

ALTIS: A fast and automatic lung and trachea CT‐image segmentation method

et al. 2019

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“…A diverse range of lung segmentation techniques for CT images has been proposed. They can be categorised into rule-based [8][9][10][11], atlas-based [12][13][14], ML-based [15][16][17][18][19], and hybrid approaches [20][21][22][23][24]. The lung appears as a low-density but high-contrast region on an x-ray-based image, such as CT, so that thresholding and atlas segmentation methods lead to good results in cases with only mild or low-density pathologies such as emphysema [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem

et al. 2020

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Background: Automated segmentation of anatomical structures is a crucial step in image analysis. For lung segmentation in computed tomography, a variety of approaches exists, involving sophisticated pipelines trained and validated on different datasets. However, the clinical applicability of these approaches across diseases remains limited. Methods: We compared four generic deep learning approaches trained on various datasets and two readily available lung segmentation algorithms. We performed evaluation on routine imaging data with more than six different disease patterns and three published data sets. Results: Using different deep learning approaches, mean Dice similarity coefficients (DSCs) on test datasets varied not over 0.02. When trained on a diverse routine dataset (n = 36), a standard approach (U-net) yields a higher DSC (0.97 ± 0.05) compared to training on public datasets such as the Lung Tissue Research Consortium (0.94 ± 0.13, p = 0.024) or Anatomy 3 (0.92 ± 0.15, p = 0.001). Trained on routine data (n = 231) covering multiple diseases, U-net compared to reference methods yields a DSC of 0.98 ± 0.03 versus 0.94 ± 0.12 (p = 0.024). Conclusions: The accuracy and reliability of lung segmentation algorithms on demanding cases primarily relies on the diversity of the training data, highlighting the importance of data diversity compared to model choice. Efforts in developing new datasets and providing trained models to the public are critical. By releasing the trained model under General Public License 3.0, we aim to foster research on lung diseases by providing a readily available tool for segmentation of pathological lungs.

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“…Mansoor et al [ 6 ] segmented pathological lungs from CT scans by combining region-based segmentation with a local descriptor based classification. Chen et al [ 7 ] segmented pathological lungs from 3D low-dose CT images using an eigenspace sparse shape composition by integrating a sparse shape composition with an eigenvector space shape prior model. Revathi et al [ 8 ] introduced a pathological lung identification system, where, FC was used for segmenting lungs with a diverse range of lung abnormalities, RF was then applied to refine the segmentation by identifying pathology and non-pathology tissues according to the features extracted from the gray-level co-occurrence matrix (GLCM), gray level run length matrices, histograms and so on.…”

Section: Related Workmentioning

confidence: 99%

Pathological lung segmentation based on random forest combined with deep model and multi-scale superpixels

Liu

Zhao

Xie

et al. 2020

Neural Process Lett

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Accurate segmentation of lungs in pathological thoracic computed tomography (CT) scans plays an important role in pulmonary disease diagnosis. However, it is still a challenging task due to the variability of pathological lung appearances and shapes. In this paper, we proposed a novel segmentation algorithm based on random forest (RF), deep convolutional network, and multi-scale superpixels for segmenting pathological lungs from thoracic CT images accurately. A pathological thoracic CT image is first segmented based on multi-scale superpixels, and deep features, texture, and intensity features extracted from superpixels are taken as inputs of a group of RF classifiers. With the fusion of classification results of RFs by a fractional-order gray correlation approach, we capture an initial segmentation of pathological lungs. We finally utilize a divide-and-conquer strategy to deal with segmentation refinement combining contour correction of left lungs and region repairing of right lungs. Our algorithm is tested on a group of thoracic CT images affected with interstitial lung diseases. Experiments show that our algorithm can achieve a high segmentation accuracy with an average DSC of 96.45% and PPV of 95.07%. Compared with several existing lung segmentation methods, our algorithm exhibits a robust performance on pathological lung segmentation. Our algorithm can be employed reliably for lung field segmentation of pathologic thoracic CT images with a high accuracy, which is helpful to assist radiologists to detect the presence of pulmonary diseases and quantify its shape and size in regular clinical practices.

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Automatic Pathological Lung Segmentation in Low-Dose CT Image Using Eigenspace Sparse Shape Composition

Cited by 32 publications

References 34 publications

ALTIS: A fast and automatic lung and trachea CT‐image segmentation method

ALTIS: A fast and automatic lung and trachea CT‐image segmentation method

Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem

Pathological lung segmentation based on random forest combined with deep model and multi-scale superpixels

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