Segmentation of small ground glass opacity pulmonary nodules based on Markov random field energy and Bayesian probability difference

Zhang, Shaorong; Chen, Xiangmeng; Zhu, Zhibin; Bao, Feng; Chen, Yehang; Long, Wansheng

doi:10.1186/s12938-020-00793-0

Cited by 9 publications

(7 citation statements)

References 32 publications

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“…Lung parenchyma segmentation can be modeled using cost functions and probabilistic models, exploring known anatomical landmarks and patient specific shape knowledge [16,[100][101][102][103][104][105][106][107][108]122]. Prior contours and shapes can be adapted to the intended target using the active contour approach, where their form is iteratively guided by an energy function [15,[109][110][111][112][113]. Also included in this classification are atlas-based methods, where the input is registered in one or multiple atlases representative of the problem [16].…”

Section: Shape or Model-based Methodsmentioning

confidence: 99%

A Systematic Review of Automated Segmentation Methods and Public Datasets for the Lung and its Lobes and Findings on Computed Tomography Images

et al. 2022

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Objectives: Automated computational segmentation of the lung and its lobes and findings in X-Ray based computed tomography (CT) images is a challenging problem with important applications, including medical research, surgical planning, and diagnostic decision support. With the increase in large imaging cohorts and the need for fast and robust evaluation of normal and abnormal lungs and their lobes, several authors have proposed automated methods for lung assessment on CT images. In this paper we intend to provide a comprehensive summarization of these methods. Methods: We used a systematic approach to perform an extensive review of automated lung segmentation methods. We chose Scopus, PubMed, and Scopus to conduct our review and included methods that perform segmentation of the lung parenchyma, lobes or internal disease related findings. The review was not limited by date, but rather by only including methods providing quantitative evaluation. Results: We organized and classified all 234 included articles into various categories according to methodological similarities among them. We provide summarizations of quantitative evaluations, public datasets, evaluation metrics, and overall statistics indicating recent research directions of the field. Conclusions: We noted the rise of data-driven models in the last decade, especially due to the deep learning trend, increasing the demand for high-quality data annotation. This has instigated an increase of semi-supervised and uncertainty guided works that try to be less dependent on human annotation. In addition, the question of how to evaluate the robustness of data-driven methods remains open, given that evaluations derived from specific datasets are not general.

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Section: Shape or Model-based Methodsmentioning

confidence: 99%

A Systematic Review of Automated Segmentation Methods and Public Datasets for the Lung and its Lobes and Findings on Computed Tomography Images

et al. 2022

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“…Gaussian mixture models (GMMs) have been used in a wide variety of clustering applications [12,13,14,15,16,17,18] due to their powerful mathematical characteristics. Confidence regions are used to diagnose the detection data x in GMMs, and the optimal confidence regions is determined based on Mahalanobis distance following the Chi-square (χ 2 ) distribution.…”

Section: Csmentioning

confidence: 99%

A Novel Intelligent System Based on Adjustable Classifier Models for Diagnosing Heart Sounds

Sun¹,

Huang

Zhang

et al. 2021

Preprint

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Background: As an eﬃcient method, heart sounds (HSs) analysis by classifying the features extracted from the four-stage sequence consisting of the ﬁrst heart sound (S1), second heart sound (S2), duration from S1 to S2 and duration from S2 to S1, has been widely used to diagnose heart disease and evaluate heart functions. However, the feature is diﬃcult to be extracted with high accuracy due to the four stages segmented from HSs with low accuracy; the ﬁxed classiﬁers achieved by training the features from the old samples cannot better ﬁt the features from the new ones because they are not adjusted with the incremental features. Thus, a novel intelligent diagnostic system, the innovations of which are primarily reﬂected in the automatic feature extraction and adjustable classiﬁer models, is proposed to realize the diagnosis of heart diseases with higher accuracy. Methods: The three stages of the proposed system are summarized as follows. In stage 1, the short time modiﬁed Hilbert transform (STMHT)-based curve is used to segment and extract the ﬁrst complex sound (CS1) and second complex sound (CS2). In stage 2, the envelopes CS1FE and CS2FE for periods CS1 and CS2 are obtained via a novel method, and the frequency features are automatically extracted from CS1FE and CS2FE by setting diﬀerent threshold value (Thv) lines. Finally, the principal component analysis (PCA)-based ﬁrst three principal components γ1, γ2, and γ3 are determined as the diagnostic features. In stage 3, a Gaussian mixture model (GMM)-based objective function fet(x) is generated. Then, the χ2 distribution for component k is determined by calculating the Mahalanobis distance from x to the class mean µk for component k, and the conﬁdence region of component k is determined by adjusting the optimal conﬁdence level βk and used as the criterion to diagnose HSs. Results: The performance evaluation was validated by sounds from online HS databases and clinical heart databases. The accuracy of the proposed method was compared to the accuracies of other well-known classiﬁers, and the highest classiﬁcation accuracies of 99.43%, 98.93%, 99.13%, 99.85%, 98.62%, 99.67% and 99.91% in the detection of MR, MS, ASD, NM, AS, AR and VSD sounds were achieved by setting βk(k = 1,2,...,7) to 0.87,0.65,0.67,0.65,0.67,0.79 and 0.87, respectively. Conclusions: This proposed intelligent diagnosis system provides an eﬃcient way to diagnose seven types of heart diseases. In addition, methods to manage the sounds (such as some ASD sounds) when CS1 and CS2 cannot be segmented and extracted via the STMHT method will be explored in the future to characterize the physical meanings of the frequency components and to build a model of the secondary curve in the frequency domain.

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“…Another challenge lies in the segmentation of lung nodules, which is found in the case of nodules with small diameter and intensity comparable to that of the surrounding noise, which thereby hinders the down-sampling potential of the segmentation network, where the network cannot extract more in-depth semantic network features [ 4 ]. It significantly impacts the accuracy of the extraction of feature maps of large nodules.…”

Section: Introductionmentioning

confidence: 99%

A Bi-FPN-Based Encoder–Decoder Model for Lung Nodule Image Segmentation

Annavarapu

Parisapogu²,

Keetha

et al. 2023

Diagnostics

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Early detection and analysis of lung cancer involve a precise and efficient lung nodule segmentation in computed tomography (CT) images. However, the anonymous shapes, visual features, and surroundings of the nodules as observed in the CT images pose a challenging and critical problem to the robust segmentation of lung nodules. This article proposes a resource-efficient model architecture: an end-to-end deep learning approach for lung nodule segmentation. It incorporates a Bi-FPN (bidirectional feature network) between an encoder and a decoder architecture. Furthermore, it uses the Mish activation function and class weights of masks with the aim of enhancing the efficiency of the segmentation. The proposed model was extensively trained and evaluated on the publicly available LUNA-16 dataset consisting of 1186 lung nodules. To increase the probability of the suitable class of each voxel in the mask, a weighted binary cross-entropy loss of each sample of training was utilized as network training parameter. Moreover, on the account of further evaluation of robustness, the proposed model was evaluated on the QIN Lung CT dataset. The results of the evaluation show that the proposed architecture outperforms existing deep learning models such as U-Net with a Dice Similarity Coefficient of 82.82% and 81.66% on both datasets.

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Segmentation of small ground glass opacity pulmonary nodules based on Markov random field energy and Bayesian probability difference

Cited by 9 publications

References 32 publications

A Systematic Review of Automated Segmentation Methods and Public Datasets for the Lung and its Lobes and Findings on Computed Tomography Images

A Systematic Review of Automated Segmentation Methods and Public Datasets for the Lung and its Lobes and Findings on Computed Tomography Images

A Novel Intelligent System Based on Adjustable Classifier Models for Diagnosing Heart Sounds

A Bi-FPN-Based Encoder–Decoder Model for Lung Nodule Image Segmentation

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