Dr. Chaoqiong Ma Profile

Dr. Chaoqiong Ma

at Emory Univ

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Publications (3)

Proceedings Article | 2 April 2024 Poster + Paper

Dose-weighted proton linear energy transfer map generation using a deep learning framework

Yuan Gao, Chih-Wei Chang, Shaoyan Pan, Junbo Peng, Chaoqiong Ma, Pretesh Patel, Justin Roper, Jun Zhou, Xiaofeng Yang

Proceedings Volume 12930, 1293023 (2024) https://doi.org/10.1117/12.3006962

KEYWORDS: Proton therapy, Deep learning, Education and training, Monte Carlo methods, Gallium nitride, Tissues, Energy transfer, Radiotherapy, Prostate cancer, Performance modeling

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Proceedings Article | 10 April 2023 Presentation + Paper

Multi-structure segmentation on cardiac MRI using multilayer perceptron mixer network

Shaoyan Pan, Chaoqiong Ma, Chih-Wei Chang, Jacob Wynne, Justin Roper, Tian Liu, Xiaofeng Yang

Proceedings Volume 12468, 124680D (2023) https://doi.org/10.1117/12.2653944

KEYWORDS: Image segmentation, Magnetic resonance imaging, Education and training, Transformers, Medical imaging, Cardiovascular magnetic resonance imaging, Image processing, Semantics, Visualization, Myocardium

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In this work, we propose MLP-Vnet, a token-based U-shaped multilayer linear perceptron-mixer (MLP-Mixer) network, incorporating a convolutional neural network for multi-structure segmentation on cardiac magnetic resonance imaging (MRI). The proposed MLP-Vnet is composed of an encoder and decoder. Taking an MRI scan as input, the semantic features are extracted by the encoder with one early convolutional block followed by four consecutive MLP-Mixer blocks. Then, the extracted features are passed to the decoder with mirrored architecture of the encoder to form a N-classes segmentation map. We evaluated our proposed network on the Automated Cardiac Diagnosis Challenge (ACDC) dataset. The performance of the network was assessed in terms of the volume- and surface-based similarities between the predicted contours and the manually delineated ground-truth contours, and computational efficiency. The volume-based similarities were measured by the Dice score coefficient (DSC), sensitivity, and precision. The surface-based similarities were measured by Hausdorff distance (HD), mean surface distance (MSD), and residual mean square distance (RMSD). The performance of the MLP-Vnet was compared with four state-of-the-art networks. The proposed network demonstrated statistically superior DSC and superior sensitivity or precision on all the three structures to the competing networks (p-value < 0.05): average DSC of 0.904, sensitivity of 0.908 and precision of 0.902 among all structures. The best surfaceased similarities were also demonstrated by the MLP-Vnet: average HD = 3.266 mm, MSD = 0.684 mm, and RMSD = 1.487 mm. Compared to the competing networks, the MLP-Vnet showed the shortest training time (7.32 hours) inference time per patient (3.12 seconds). The proposed MLP-Vnet is capable of using reasonable number of trainable parameters to solve the segmentation task on the cardiac MRI scans more quickly and accurately than the state-ofthe- art networks. This novel network could be a promising tool for accurate and efficient cardiac MRI segmentation to assist cardiac diagnosis and treatment decision making.

Proceedings Article | 3 April 2023 Poster + Paper

Deep learning-based multi-organ CT segmentation with adversarial data augmentation

Shaoyan Pan, Shao-Yuan Lo, Min Huang, Chaoqiong Ma, Jacob Wynne, Tonghe Wang, Tian Liu, Xiaofeng Yang

Proceedings Volume 12464, 124642D (2023) https://doi.org/10.1117/12.2653970

KEYWORDS: Image segmentation, Kidney, Liver, Stomach, Gallbladder, Education and training, Deep learning, Aorta, Adversarial training, Computed tomography

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In this work, we propose an adversarial attack-based data augmentation method to improve the deep-learning-based segmentation algorithm for the delineation of Organs-At-Risk (OAR) in abdominal Computed Tomography (CT) to facilitate radiation therapy. We introduce Adversarial Feature Attack for Medical Image (AFA-MI) augmentation, which forces the segmentation network to learn out-of-distribution statistics and improve generalization and robustness to noises. AFA-MI augmentation consists of three steps: 1) generate adversarial noises by Fast Gradient Sign Method (FGSM) on the intermediate features of the segmentation network’s encoder; 2) inject the generated adversarial noises into the network, intentionally compromising performance; 3) optimize the network with both clean and adversarial features. The effectiveness of the AFA-MI augmentation was validated on nnUnet. Experiments are conducted segmenting the heart, left and right kidney, liver, left and right lung, spinal cord, and stomach in an institutional dataset collected from 60 patients. We firstly evaluate the AFA-MI augmentation using nnUnet and Token-based Transformer Vnet (TT-Vnet) on the test data from a public abdominal dataset and an institutional dataset. In addition, we validate how AFA-MI affects the networks’ robustness to the noisy data by evaluating the networks with added Gaussian noises of varying magnitudes to the institutional dataset. Network performance is quantitatively evaluated using Dice Similarity Coefficient (DSC) for volume-based accuracy. Also, Hausdorff Distance (HD) is applied for surface-based accuracy. On the public dataset, nnUnet with AFA-MI achieves DSC = 0.85 and HD = 6.16 millimeters (mm); and TT-Vnet achieves DSC = 0.86 and HD = 5.62 mm. On the robustness experiment with the institutional data, AFA-MI is observed to improve the segmentation DSC score ranging from 0.055 to 0.010 across all organs relative to clean inputs. AFA-MI augmentation further improves all contour accuracies up to 0.527 as measured by the DSC score when tested on images with Gaussian noises. AFA-MI augmentation is therefore demonstrated to improve segmentation performance and robustness in CT multi-organ segmentation.

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