Deep Residual 3D U-Net for Joint Segmentation and Texture Classification of Nodules in Lung

Rassadin, Alexandr G.

doi:10.1007/978-3-030-50516-5_37

Cited by 5 publications

(3 citation statements)

References 23 publications

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“…This issue can be solved by adding a residual unit to U-Net, which can make use of the merits of the residual network [ 6 ]. A deep residual U-Net model has been used for lung segmentation in CT scans [ 9 ], joint segmentation in CT scans [ 18 ], and vulnerable plaque segmentation in optical coherence tomography images [ 19 ]. These prior studies consistently reported the high segmentation performance of a deep residual U-Net model.…”

Section: Discussionmentioning

confidence: 99%

A Deep Residual U-Net Algorithm for Automatic Detection and Quantification of Ascites on Abdominopelvic Computed Tomography Images Acquired in the Emergency Department: Model Development and Validation

Ko¹,

Huh²,

Kim³

et al. 2022

J Med Internet Res

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Background Detection and quantification of intra-abdominal free fluid (ie, ascites) on computed tomography (CT) images are essential processes for finding emergent or urgent conditions in patients. In an emergency department, automatic detection and quantification of ascites will be beneficial. Objective We aimed to develop an artificial intelligence (AI) algorithm for the automatic detection and quantification of ascites simultaneously using a single deep learning model (DLM). Methods We developed 2D DLMs based on deep residual U-Net, U-Net, bidirectional U-Net, and recurrent residual U-Net (R2U-Net) algorithms to segment areas of ascites on abdominopelvic CT images. Based on segmentation results, the DLMs detected ascites by classifying CT images into ascites images and nonascites images. The AI algorithms were trained using 6337 CT images from 160 subjects (80 with ascites and 80 without ascites) and tested using 1635 CT images from 40 subjects (20 with ascites and 20 without ascites). The performance of the AI algorithms was evaluated for diagnostic accuracy of ascites detection and for segmentation accuracy of ascites areas. Of these DLMs, we proposed an AI algorithm with the best performance. Results The segmentation accuracy was the highest for the deep residual U-Net model with a mean intersection over union (mIoU) value of 0.87, followed by U-Net, bidirectional U-Net, and R2U-Net models (mIoU values of 0.80, 0.77, and 0.67, respectively). The detection accuracy was the highest for the deep residual U-Net model (0.96), followed by U-Net, bidirectional U-Net, and R2U-Net models (0.90, 0.88, and 0.82, respectively). The deep residual U-Net model also achieved high sensitivity (0.96) and high specificity (0.96). Conclusions We propose a deep residual U-Net–based AI algorithm for automatic detection and quantification of ascites on abdominopelvic CT scans, which provides excellent performance.

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Section: Discussionmentioning

confidence: 99%

A Deep Residual U-Net Algorithm for Automatic Detection and Quantification of Ascites on Abdominopelvic Computed Tomography Images Acquired in the Emergency Department: Model Development and Validation

Ko¹,

Huh²,

Kim³

et al. 2022

J Med Internet Res

View full text Add to dashboard Cite

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“…Residual UNet (35.0M), 44 , 45 grouping every couple of convolutions with the aim to stabilize the training of deeper networks,…”

Section: Methodsmentioning

confidence: 99%

“…• UNet (16.3M), 43 the baseline of most segmentation models, consisting of an auto-encoder architecture with skip connection between layers of the same depth, • Residual UNet (35.0M), 44,45 grouping every couple of convolutions with the aim to stabilize the training of deeper networks, • and their counterparts DUNet (16.7M) and residual DUNet (35.5M), 14 replacing the second to last skip connection with a dilated dense network of convolutions to improve the information flow between the encoder and the decoder.…”

Section: Comparison With Analogous Convolutional Modelsmentioning

confidence: 99%

Attention-gated 3D CapsNet for robust hippocampal segmentation

Poiret,

Bouyeure,

Patil

et al. 2024

J. Med. Imag.

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.PurposeThe hippocampus is organized in subfields (HSF) involved in learning and memory processes and widely implicated in pathologies at different ages of life, from neonatal hypoxia to temporal lobe epilepsy or Alzheimer’s disease. Getting a highly accurate and robust delineation of sub-millimetric regions such as HSF to investigate anatomo-functional hypotheses is a challenge. One of the main difficulties encountered by those methodologies is related to the small size and anatomical variability of HSF, resulting in the scarcity of manual data labeling. Recently introduced, capsule networks solve analogous problems in medical imaging, providing deep learning architectures with rotational equivariance. Nonetheless, capsule networks are still two-dimensional and unassessed for the segmentation of HSF.ApproachWe released a public 3D Capsule Network (3D-AGSCaps, https://github.com/clementpoiret/3D-AGSCaps) and compared it to equivalent architectures using classical convolutions on the automatic segmentation of HSF on small and atypical datasets (incomplete hippocampal inversion, IHI). We tested 3D-AGSCaps on three datasets with manually labeled hippocampi.ResultsOur main results were: (1) 3D-AGSCaps produced segmentations with a better Dice Coefficient compared to CNNs on rotated hippocampi (p=0.004, cohen’s d=0.179); (2) on typical subjects, 3D-AGSCaps produced segmentations with a Dice coefficient similar to CNNs while having 15 times fewer parameters (2.285M versus 35.069M). This may greatly facilitate the study of atypical subjects, including healthy and pathological cases like those presenting an IHI.ConclusionWe expect our newly introduced 3D-AGSCaps to allow a more accurate and fully automated segmentation on atypical populations, small datasets, as well as on and large cohorts where manual segmentations are nearly intractable.

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