PET image harmonization using smoothing-cycleGAN

Thabit, Abdullah; Li, Shenpeng; Williams, Rob; Villemagne, Victor L.; Rowe, Christopher C.; Doré, Vincent; Bourgeat, Pierrick

doi:10.1117/12.2611353

Cited by 3 publications

(1 citation statement)

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“…20 These frameworks and their variants are selectively used in different situations depending on the data properties. [21][22][23] Recently, multiple attention-based approaches achieved outstanding performance on the natural I2I scenario, [24][25][26][27] and are being incrementally adopted for medical image harmonization. 28,29 For example, based on CycleGAN, UGATIT 26 leverages attention modules with adaptive layer-instance normalization (AdaLIN) to guide the model to focus on regions with the largest style differences.…”

Section: Introductionmentioning

confidence: 99%

Novel application of the attention mechanism on medical image harmonization

Yao

Lou

et al. 2023

Medical Imaging 2023: Image Processing

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Medical image harmonization aims to transform the image 'style' among heterogeneous datasets while preserving the anatomical content. It enables data-sensitive learning-based approaches to fully leverage the data power of large multisite datasets with different image acquisitions. Recently, the attention mechanism has achieved excellent performance on the image-to-image (I2I) translation of natural images. In this work, we further explore the potential of leveraging the attention mechanism to improve the performance of medical image harmonization. Here, we introduce two attention-based frameworks with outstanding performance in the natural I2I scenario in the context of cross-scanner MRI harmonization for the first time. We compare them with the existing commonly used harmonization frameworks by evaluating their ability to enhance the performance of the downstream subcortical segmentation task on T1-weighted (T1w) MRI datasets from 1.5T vs. 3T scanners. Both qualitative and quantitative results prove that the attention mechanism contributes to a noticeable improvement in harmonization ability.

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