Bi-parametric prostate MR image synthesis using pathology and sequence-conditioned stable diffusion

• URL: http://arxiv.org/abs/2303.02094v1
• Date: Fri, 3 Mar 2023 17:24:39 GMT
• Title: Bi-parametric prostate MR image synthesis using pathology and sequence-conditioned stable diffusion
• Authors: Shaheer U. Saeed, Tom Syer, Wen Yan, Qianye Yang, Mark Emberton, Shonit Punwani, Matthew J. Clarkson, Dean C. Barratt, Yipeng Hu
• Abstract summary: We propose an image synthesis mechanism for multi-sequence prostate MR images conditioned on text. We generate paired bi-parametric images conditioned on images conditioned on paired data. We validate our method using 2D image slices from real suspected prostate cancer patients.
• Score: 3.290987481767681
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose an image synthesis mechanism for multi-sequence prostate MR images conditioned on text, to control lesion presence and sequence, as well as to generate paired bi-parametric images conditioned on images e.g. for generating diffusion-weighted MR from T2-weighted MR for paired data, which are two challenging tasks in pathological image synthesis. Our proposed mechanism utilises and builds upon the recent stable diffusion model by proposing image-based conditioning for paired data generation. We validate our method using 2D image slices from real suspected prostate cancer patients. The realism of the synthesised images is validated by means of a blind expert evaluation for identifying real versus fake images, where a radiologist with 4 years experience reading urological MR only achieves 59.4% accuracy across all tested sequences (where chance is 50%). For the first time, we evaluate the realism of the generated pathology by blind expert identification of the presence of suspected lesions, where we find that the clinician performs similarly for both real and synthesised images, with a 2.9 percentage point difference in lesion identification accuracy between real and synthesised images, demonstrating the potentials in radiological training purposes. Furthermore, we also show that a machine learning model, trained for lesion identification, shows better performance (76.2% vs 70.4%, statistically significant improvement) when trained with real data augmented by synthesised data as opposed to training with only real images, demonstrating usefulness for model training.

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