Comparison of convolutional neural network training strategies for cone-beam CT image segmentation

Minnema, Jordi; Wolff, Jan; Koivisto, Juha; Lucka, Felix; Batenburg, Kees Joost; Forouzanfar, Tymour; Eijnatten, Maureen van

doi:10.1016/j.cmpb.2021.106192

Cited by 18 publications

(14 citation statements)

References 24 publications

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“…2.5D CNN has been developed to solve the memory consumption problem of 3D models ( Zhang et al, 2021 ). 2.5D CNN has at least three approaches ( Zhang et al, 2021 ; Minnema et al, 2021 ). The first is a combination of outputs of 2D CNNs in three orthogonal planes (axial, coronal, and sagittal) with majority voting.…”

Section: Discussionmentioning

confidence: 99%

A 3D deep learning approach to epicardial fat segmentation in non-contrast and post-contrast cardiac CT images

Siriapisith

Kusakunniran

Haddawy

2021

PeerJ Computer Science

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Epicardial fat (ECF) is localized fat surrounding the heart muscle or myocardium and enclosed by the thin-layer pericardium membrane. Segmenting the ECF is one of the most difficult medical image segmentation tasks. Since the epicardial fat is infiltrated into the groove between cardiac chambers and is contiguous with cardiac muscle, segmentation requires location and voxel intensity. Recently, deep learning methods have been effectively used to solve medical image segmentation problems in several domains with state-of-the-art performance. This paper presents a novel approach to 3D segmentation of ECF by integrating attention gates and deep supervision into the 3D U-Net deep learning architecture. The proposed method shows significant improvement of the segmentation performance, when compared with standard 3D U-Net. The experiments show excellent performance on non-contrast CT datasets with average Dice scores of 90.06%. Transfer learning from a pre-trained model of a non-contrast CT to contrast-enhanced CT dataset was also performed. The segmentation accuracy on the contrast-enhanced CT dataset achieved a Dice score of 88.16%.

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Section: Discussionmentioning

confidence: 99%

A 3D deep learning approach to epicardial fat segmentation in non-contrast and post-contrast cardiac CT images

Siriapisith

Kusakunniran

Haddawy

2021

PeerJ Computer Science

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“…Baker et al [23] applied a hybrid NN model, CNN+LSTM, for estimating blood pressure from raw electrocardiogram and photoplethysmogram waveforms and demonstrated that ML techniques are an effective approach for blood pressure estimation and are ready for implementing them into wearable devices. Minnema et al [24] evaluated different CNN training strategies for CT image segmentation. The results indicate that analyzing the structure of the images helps performance improvement.…”

Section: Related Workmentioning

confidence: 99%

“…The literature review [9,24] inspired our study of personalized health care prediction by analyzing the groups of patients for comprehensive care needs. The literature review [27] has demonstrated that multi-classifiers could yield high detection performance.…”

Section: Lstm-based Personalized Care Prediction Systemmentioning

confidence: 99%

Analyzing Groups of Inpatients’ Healthcare Needs to Improve Service Quality and Sustainability

et al. 2021

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The trend towards personalized healthcare has led to an increase in applying deep learning techniques to improve healthcare service quality and sustainability. With the increasing number of patients with multiple comorbidities, they need comprehensive care services, where comprehensive care is a synonym for complete patient care to respond to a patient’s physical, emotional, social, economic, and spiritual needs, and, as such, an efficient prediction system for comprehensive care suggestions could help physicians and healthcare providers in making clinical judgement. The experiment dataset contained a total of 2.9 million electrical medical records (EMRs) from 250 thousand hospitalized patients collected retrospectively from a first-tier medical center in Taiwan, where the EMRs were de-identified and anonymized and where 949 cases had received comprehensive care. Recurrent neural networks (RNNs) are designed for analyzing time-series data but are still lacking in studying predicting personalized healthcare. Furthermore, in most cases, the collected evaluation data are imbalanced with a small portion of positive cases. This study examined the impact of imbalanced data in model training and suggested an effective approach to handle such a situation. To address the above-mentioned research issue, this study analyzed the care need in the different patient groupings, proposed a personalized care suggestion system by applying RNN models, and developed an efficient model training scheme for building AI-assisted prediction models. This study observed several findings: (1) the data resampling schemes could mitigate the impact of imbalanced data on model training, and the under-sampling scheme achieved the best performance with an ACC of 99.80%, a PPV of 70.18%, an NPV of 99.87%, a recall of 82.91%, and an F1 score of 0.7602, while the model trained with the original data had a very low PPV of 6.42% and a low F1 score of 0.1116; (2) patient clustering with multi-classier could predict comprehensive care needs efficiently with an ACC of 99.87%, a PPV of 77.90%, an NPV of 99.90%, a recall of 92.19%, and an F1 score of 0.8404; (3) the proposed long short-term memory (LSTM) prediction model achieved the best overall performance with an ACC of 99.80%, a PPV of 70.18%, an NPV of 99.87%, a recall of 82.91%, and an F1 score of 0.7602.

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“…Several U-Nets including 2D U-Net 20,21 , 2.5D U-Net 22 , and 3D U-Net 23 have been proposed for CBCT segmentation. A variant of 2.5D U-Net using majority voting of 2D U-Nets trained by 3 orthogonal imaging planes has been shown to outperform any single U-Net for maxillary and mandibular bony structure segmentation on CBCT 24 . To the best of our knowledge, CT using a 3.5D U-Net integrating 2D U-Nets, 2.5D U-Net, and 3D U-Net has never been documented yet.…”

mentioning

confidence: 99%

“…Finally, we applied majority voting to create 4 additional U-Nets. Via combining the predictions of 2D U-Nets trained from each of three orthogonal slices 24 using majority voting, a 2.5Dv U-Net was generated. Three additional 3.5D U-Nets Table 1.…”

mentioning

confidence: 99%

Improving performance of deep learning models using 3.5D U-Net via majority voting for tooth segmentation on cone beam computed tomography

Hsu

Yuh

Lin

et al. 2022

Sci Rep

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Deep learning allows automatic segmentation of teeth on cone beam computed tomography (CBCT). However, the segmentation performance of deep learning varies among different training strategies. Our aim was to propose a 3.5D U-Net to improve the performance of the U-Net in segmenting teeth on CBCT. This study retrospectively enrolled 24 patients who received CBCT. Five U-Nets, including 2Da U-Net, 2Dc U-Net, 2Ds U-Net, 2.5Da U-Net, 3D U-Net, were trained to segment the teeth. Four additional U-Nets, including 2.5Dv U-Net, 3.5Dv5 U-Net, 3.5Dv4 U-Net, and 3.5Dv3 U-Net, were obtained using majority voting. Mathematical morphology operations including erosion and dilation (E&D) were applied to remove diminutive noise speckles. Segmentation performance was evaluated by fourfold cross validation using Dice similarity coefficient (DSC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV). Kruskal–Wallis test with post hoc analysis using Bonferroni correction was used for group comparison. P < 0.05 was considered statistically significant. Performance of U-Nets significantly varies among different training strategies for teeth segmentation on CBCT (P < 0.05). The 3.5Dv5 U-Net and 2.5Dv U-Net showed DSC and PPV significantly higher than any of five originally trained U-Nets (all P < 0.05). E&D significantly improved the DSC, accuracy, specificity, and PPV (all P < 0.005). The 3.5Dv5 U-Net achieved highest DSC and accuracy among all U-Nets. The segmentation performance of the U-Net can be improved by majority voting and E&D. Overall speaking, the 3.5Dv5 U-Net achieved the best segmentation performance among all U-Nets.

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Comparison of convolutional neural network training strategies for cone-beam CT image segmentation

Cited by 18 publications

References 24 publications

A 3D deep learning approach to epicardial fat segmentation in non-contrast and post-contrast cardiac CT images

A 3D deep learning approach to epicardial fat segmentation in non-contrast and post-contrast cardiac CT images

Analyzing Groups of Inpatients’ Healthcare Needs to Improve Service Quality and Sustainability

Improving performance of deep learning models using 3.5D U-Net via majority voting for tooth segmentation on cone beam computed tomography

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