Heart chamber segmentation from CT using convolutional neural networks

Dormer, James D.; Ma, Ling; Halicek, Martin; Reilly, Carolyn Miller; Schreibmann, Eduard; Fei, Baowei

doi:10.1117/12.2293554

Cited by 24 publications

(19 citation statements)

References 15 publications

(14 reference statements)

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“…[10][11][12] Other studies have approached this problem using similar methods as the one presented, but many share the same dataset of healthy adult patients, and still others use a dataset containing only a few patients. [13][14][15][16][17] Our dataset is unique in not only its larger size, but also its inclusion of children and adolescents as well as adults, all of whom present with cardiac pathology. All of the mentioned factors have been included in this paper to yield a more general and realistic result for RV segmentation, and the confounding factors have been retained in the dataset for objectivity.…”

Section: Resultsmentioning

confidence: 99%

Fully automated segmentation of the right ventricle in patients with repaired Tetralogy of Fallot using U-Net

Tran

Halicek

Dormer

et al. 2020

Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging

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Cardiac magnetic resonance (CMR) imaging is considered the standard imaging modality for volumetric analysis of the right ventricle (RV), an especially important practice in the evaluation of heart structure and function in patients with repaired Tetralogy of Fallot (rTOF). In clinical practice, however, this requires time-consuming manual delineation of the RV endocardium in multiple 2-dimensional (2D) slices at multiple phases of the cardiac cycle. In this work, we employed a U-Net based 2D convolutional neural network (CNN) classifier in the fully automatic segmentation of the RV blood pool. Our dataset was comprised of 5,729 short-axis cine CMR slices taken from 100 individuals with rTOF. Training of our CNN model was performed on images from 50 individuals while validation was performed on images from 10 individuals. Segmentation results were evaluated by Dice similarity coefficient (DSC) and Hausdorff distance (HD). Use of the CNN model on our testing group of 40 individuals yielded a median DSC of 90% and a median 95 th percentile HD of 5.1 mm, demonstrating good performance in these metrics when compared to literature results. Our preliminary results suggest that our deep learning-based method can be effective in automating RV segmentation.

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Section: Resultsmentioning

confidence: 99%

Fully automated segmentation of the right ventricle in patients with repaired Tetralogy of Fallot using U-Net

Tran

Halicek

Dormer

et al. 2020

Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging

Self Cite

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“…Deep neural networks have the potential to overcome these drawbacks and have proven outstanding performance in several areas of CT imaging such as segmentation, registration, denoising, CT artifact reduction, or sparse view CT …”

Section: Introductionmentioning

confidence: 99%

Real‐time scatter estimation for medical CT using the deep scatter estimation: Method and robustness analysis with respect to different anatomies, dose levels, tube voltages, and data truncation

et al. 2018

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Purpose: X-ray scattering leads to CT images with a reduced contrast, inaccurate CT values as well as streak and cupping artifacts. Therefore, scatter correction is crucial to maintain the diagnostic value of CT and CBCT examinations. However, existing approaches are not able to combine both high accuracy and high computational performance. Therefore, we propose the deep scatter estimation (DSE): a deep convolutional neural network to derive highly accurate scatter estimates in real time. Methods: Gold standard scatter estimation approaches rely on dedicated Monte Carlo (MC) photon transport codes. However, being computationally expensive, MC methods cannot be used routinely. To enable real-time scatter correction with similar accuracy, DSE uses a deep convolutional neural network that is trained to predict MC scatter estimates based on the acquired projection data. Here, the potential of DSE is demonstrated using simulations of CBCT head, thorax, and abdomen scans as well as measurements at an experimental table-top CBCT. Two conventional computationally efficient scatter estimation approaches were implemented as reference: a kernel-based scatter estimation (KSE) and the hybrid scatter estimation (HSE). Results: The simulation study demonstrates that DSE generalizes well to varying tube voltages, varying noise levels as well as varying anatomical regions as long as they are appropriately represented within the training data. In any case the deviation of the scatter estimates from the ground truth MC scatter distribution is less than 1.8% while it is between 6.2% and 293.3% for HSE and between 11.2% and 20.5% for KSE. To evaluate the performance for real data, measurements of an anthropomorphic head phantom were performed. Errors were quantified by a comparison to a slit scan reconstruction. Here, the deviation is 278 HU (no correction), 123 HU (KSE), 65 HU (HSE), and 6 HU (DSE), respectively. Conclusions: The DSE clearly outperforms conventional scatter estimation approaches in terms of accuracy. DSE is nearly as accurate as Monte Carlo simulations but is superior in terms of speed (%10 ms/projection) by orders of magnitude.

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“…Our results are comparable to, or better than, the state-of-the-art deep learning [22,23] and multi-atlas [24,25] segmentation methods. Dormer et al [23] (avg. DSC 0.87) used only 10 CT images to train a 3D model, which is insufficient to capture image heterogeneity with fidelity.…”

Section: Discussionmentioning

confidence: 53%

Cardio-pulmonary substructure segmentation of radiotherapy computed tomography images using convolutional neural networks for clinical outcomes analysis

Haq

Hotca

Apte

et al. 2020

Physics and Imaging in Radiation Oncology

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Background and purpose: Radiation dose to the cardio-pulmonary system is critical for radiotherapy-induced mortality in non-small cell lung cancer. Our goal was to automatically segment substructures of the cardiopulmonary system for use in outcomes analyses for thoracic cancers. We built and validated a multi-label Deep Learning Segmentation (DLS) model for accurate auto-segmentation of twelve cardio-pulmonary substructures. Materials and methods: The DLS model utilized a convolutional neural network for segmenting substructures from 217 thoracic radiotherapy Computed Tomography (CT) scans. The model was built in the presence of variable image characteristics such as the absence/presence of contrast. We quantitatively evaluated the final model against expert contours for a hold-out dataset of 24 CT scans using Dice Similarity Coefficient (DSC), 95th Percentile of Hausdorff Distance and Dose-volume Histograms (DVH). DLS contours of an additional 25 scans were qualitatively evaluated by a radiation oncologist to determine their clinical acceptability. Results: The DLS model reduced segmentation time per patient from about one hour to 10 s. Quantitatively, the highest accuracy was observed for the Heart (median DSC = (0.96 (0.95-0.97)). The median DSC for the remaining structures was between 0.81 and 0.93. No statistically significant difference was found between DVH metrics of the auto-generated and manual contours (p-value 0.69). The expert judged that, on average, 85% of contours were qualitatively equivalent to state-of-the-art manual contouring. Conclusion: The cardio-pulmonary DLS model performed well both quantitatively and qualitatively for all structures. This model has been incorporated into an open-source tool for the community to use for treatment planning and clinical outcomes analysis.

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Heart chamber segmentation from CT using convolutional neural networks

Cited by 24 publications

References 15 publications

Fully automated segmentation of the right ventricle in patients with repaired Tetralogy of Fallot using U-Net

Fully automated segmentation of the right ventricle in patients with repaired Tetralogy of Fallot using U-Net

Real‐time scatter estimation for medical CT using the deep scatter estimation: Method and robustness analysis with respect to different anatomies, dose levels, tube voltages, and data truncation

Cardio-pulmonary substructure segmentation of radiotherapy computed tomography images using convolutional neural networks for clinical outcomes analysis

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