COVID-19 Lung CT Image Segmentation Using Deep Learning Methods: UNET Vs. SegNET

Saood, Adnan; Hatem, Iyad

doi:10.21203/rs.3.rs-56882/v1

Cited by 4 publications

(1 citation statement)

References 15 publications

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“…Nevertheless, the MS-D network is not the only CNN architecture capable of performing multiclass segmentation. Several other CNN architectures have been implemented to perform multiclass segmentation of anatomies in brain (Chen et al 2018;Jafari et al 2019) and lung (Novikov et al 2018;Saood and Hatem 2020).…”

Section: Discussionmentioning

confidence: 99%

Multiclass CBCT Image Segmentation for Orthodontics with Deep Learning

et al. 2021

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Accurate segmentation of the jaw (i.e., mandible and maxilla) and the teeth in cone beam computed tomography (CBCT) scans is essential for orthodontic diagnosis and treatment planning. Although various (semi)automated methods have been proposed to segment the jaw or the teeth, there is still a lack of fully automated segmentation methods that can simultaneously segment both anatomic structures in CBCT scans (i.e., multiclass segmentation). In this study, we aimed to train and validate a mixed-scale dense (MS-D) convolutional neural network for multiclass segmentation of the jaw, the teeth, and the background in CBCT scans. Thirty CBCT scans were obtained from patients who had undergone orthodontic treatment. Gold standard segmentation labels were manually created by 4 dentists. As a benchmark, we also evaluated MS-D networks that segmented the jaw or the teeth (i.e., binary segmentation). All segmented CBCT scans were converted to virtual 3-dimensional (3D) models. The segmentation performance of all trained MS-D networks was assessed by the Dice similarity coefficient and surface deviation. The CBCT scans segmented by the MS-D network demonstrated a large overlap with the gold standard segmentations (Dice similarity coefficient: 0.934 ± 0.019, jaw; 0.945 ± 0.021, teeth). The MS-D network–based 3D models of the jaw and the teeth showed minor surface deviations when compared with the corresponding gold standard 3D models (0.390 ± 0.093 mm, jaw; 0.204 ± 0.061 mm, teeth). The MS-D network took approximately 25 s to segment 1 CBCT scan, whereas manual segmentation took about 5 h. This study showed that multiclass segmentation of jaw and teeth was accurate and its performance was comparable to binary segmentation. The MS-D network trained for multiclass segmentation would therefore make patient-specific orthodontic treatment more feasible by strongly reducing the time required to segment multiple anatomic structures in CBCT scans.

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Section: Discussionmentioning

confidence: 99%

Multiclass CBCT Image Segmentation for Orthodontics with Deep Learning

et al. 2021

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Fuzzy Cognitive Map and Deep-Seg Net for Enhancing Skin Cancer Segmentation and Detection

Takale,

Gawali,

Deshmukh

et al. 2024

Lecture Notes in Networks and Systems

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Lesion segmentation in lung CT scans using unsupervised adversarial learning

Sherwani

Marzullo

Momi

et al. 2022

Med Biol Eng Comput

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Lesion segmentation in medical images is difficult yet crucial for proper diagnosis and treatment. Identifying lesions in medical images is costly and time-consuming and requires highly specialized knowledge. For this reason, supervised and semi-supervised learning techniques have been developed. Nevertheless, the lack of annotated data, which is common in medical imaging, is an issue; in this context, interesting approaches can use unsupervised learning to accurately distinguish between healthy tissues and lesions, training the network without using the annotations. In this work, an unsupervised learning technique is proposed to automatically segment coronavirus disease 2019 (COVID-19) lesions on 2D axial CT lung slices. The proposed approach uses the technique of image translation to generate healthy lung images based on the infected lung image without the need for lesion annotations. Attention masks are used to improve the quality of the segmentation further. Experiments showed the capability of the proposed approaches to segment the lesions, and it outperforms a range of unsupervised lesion detection approaches. The average reported results for the test dataset based on the metrics: Dice Score, Sensitivity, Specificity, Structure Measure, Enhanced-Alignment Measure, and Mean Absolute Error are 0.695, 0.694, 0.961, 0.791, 0.875, and 0.082 respectively. The achieved results are promising compared with the state-of-the-art and could constitute a valuable tool for future developments. Graphical abstract

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COVID-19 Lung CT Image Segmentation Using Deep Learning Methods: UNET Vs. SegNET

Cited by 4 publications

References 15 publications

Multiclass CBCT Image Segmentation for Orthodontics with Deep Learning

Multiclass CBCT Image Segmentation for Orthodontics with Deep Learning

Fuzzy Cognitive Map and Deep-Seg Net for Enhancing Skin Cancer Segmentation and Detection

Lesion segmentation in lung CT scans using unsupervised adversarial learning

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