Dr. Ivan S. Klyuzhin Profile

Dr. Ivan S. Klyuzhin

at BC Cancer Research Institute

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Publications (2)

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Proceedings Article | 7 April 2023 Poster + Paper

Clinician-interactive AI for RECIST measurements in CT imaging

Luke Polson, Ivan Klyuzhin, Ren Yuan, Monty Martin, Isaac Shiri, Habib Zaidi, Carlos Uribe, Arman Rahmim

Proceedings Volume 12463, 124633N (2023) https://doi.org/10.1117/12.2654320

KEYWORDS: Computed tomography, Artificial intelligence, Education and training, Cancer, Data modeling, Image segmentation, Lung cancer, Tumor growth modeling, Oncology, Deep learning

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The RECIST criteria are used in computed tomography (CT) imaging to assess changes in tumour burden induced by cancer therapeutics throughout treatment. One of its requirements is frequent measurement of lesion diameters , which is often time consuming for clinicians. We aimed to study clinician-interactive AI, defined as deep learning models that use image annotations as input to assist in radiological measurements. Two annotation types are compared in their enhancement of predictive capabilities: mouse clicks in the tumour region, and bounding boxes surrounding lesions. The model architectures compared in this study are the U-Net, V-Net, AH-Net, and SegRes-Net. Models were trained and tested using a non-small cell lung cancer dataset from the cancer imaging archive (TCIA) consisting of CT scans and corresponding gold-standard lesion segmentations inferred from PET/CT scans. Mouse clicks and bounding boxes, representing clinician input, were artificially generated. The absolute percent error between predicted and ground truth diameters was computed for each model architecture. Bounding box annotations yielded mean absolute percent errors of 4.9 ± 2.1 %, 7.8 ± 3.4 %, 5.6 ± 2.4 % and 5.6 ± 2.3 %, respectively, whereas models using clicks annotations yielded 17.0 ± 7.9 %, 19.8 ± 9.3 %, 21.4 ± 10.9 % and 18.1 ± 7.9%. The corresponding mean dice scores across all model architectures were 0.883 ± 0.004 and 0.760 ± 0.012 for bounding box and click annotations respectively. Models were then implemented in an AI pipeline for clinical use at the BC cancer agency using the Ascinta software package; click annotations yielded qualitatively better results than bounding box annotations.

Proceedings Article | 4 April 2022 Presentation + Paper

3D PET image generation with tumour masks using TGAN

Robert Bergen, Jean-Francois Rajotte, Fereshteh Yousefirizi, Ivan Klyuzhin, Arman Rahmim, Raymond Ng

Proceedings Volume 12032, 120321P (2022) https://doi.org/10.1117/12.2611292

KEYWORDS: Image segmentation, Positron emission tomography, Data modeling, 3D image processing, Medical imaging, Video, Head

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Training computer-vision related algorithms on medical images for disease diagnosis or image segmentation is difficult due to the lack of training data, labeled samples, and privacy concerns. For this reason, a robust generative method to create synthetic data is highly sought after. However, most three-dimensional image generators require additional image input or are extremely memory intensive. To address these issues we propose adapting video generation techniques for 3- D image generation. Using the temporal GAN (TGAN) architecture, we show we are able to generate realistic head and neck PET images. We also show that by conditioning the generator on tumour masks, we are able to control the geometry and location of the tumour in the generated images. To test the utility of the synthetic images, we train a segmentation model using the synthetic images. Synthetic images conditioned on real tumour masks are automatically segmented, and the corresponding real images are also segmented. We evaluate the segmentations using the Dice score and find the segmentation algorithm performs similarly on both datasets (0.65 synthetic data, 0.70 real data). Various radionomic features are then calculated over the segmented tumour volumes for each data set. A comparison of the real and synthetic feature distributions show that seven of eight feature distributions had statistically insignificant differences (𝑝 < 0.05). Correlation coefficients were also calculated between all radionomic features and it is shown that all of the strong statistical correlations in the real data set are preserved in the synthetic data set.

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