Dataset Information

Artificial CT images can enhance variation of case images in diagnostic radiology skills training.

ABSTRACT:

Objectives

We sought to investigate if artificial medical images can blend with original ones and whether they adhere to the variable anatomical constraints provided.

Methods

Artificial images were generated with a generative model trained on publicly available standard and low-dose chest CT images (805 scans; 39,803 2D images), of which 17% contained evidence of pathological formations (lung nodules). The test set (90 scans; 5121 2D images) was used to assess if artificial images (512 × 512 primary and control image sets) blended in with original images, using both quantitative metrics and expert opinion. We further assessed if pathology characteristics in the artificial images can be manipulated.

Results

Primary and control artificial images attained an average objective similarity of 0.78 ± 0.04 (ranging from 0 [entirely dissimilar] to 1[identical]) and 0.76 ± 0.06, respectively. Five radiologists with experience in chest and thoracic imaging provided a subjective measure of image quality; they rated artificial images as 3.13 ± 0.46 (range of 1 [unrealistic] to 4 [almost indistinguishable to the original image]), close to their rating of the original images (3.73 ± 0.31). Radiologists clearly distinguished images in the control sets (2.32 ± 0.48 and 1.07 ± 0.19). In almost a quarter of the scenarios, they were not able to distinguish primary artificial images from the original ones.

Conclusion

Artificial images can be generated in a way such that they blend in with original images and adhere to anatomical constraints, which can be manipulated to augment the variability of cases.

Critical relevance statement

Artificial medical images can be used to enhance the availability and variety of medical training images by creating new but comparable images that can blend in with original images.

Key points

• Artificial images, similar to original ones, can be created using generative networks. • Pathological features of artificial images can be adjusted through guiding the network. • Artificial images proved viable to augment the depth and broadening of diagnostic training.

SUBMITTER: Hofmeijer EIS

PROVIDER: S-EPMC10630276 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

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Publications

Artificial CT images can enhance variation of case images in diagnostic radiology skills training.

Hofmeijer Elfi Inez Saïda EIS Wu Sheng-Chih SC Vliegenthart Rozemarijn R Slump Cornelis Herman CH van der Heijden Ferdi F Tan Can Ozan CO

Insights into imaging 20231107 1

<h4>Objectives</h4>We sought to investigate if artificial medical images can blend with original ones and whether they adhere to the variable anatomical constraints provided.<h4>Methods</h4>Artificial images were generated with a generative model trained on publicly available standard and low-dose chest CT images (805 scans; 39,803 2D images), of which 17% contained evidence of pathological formations (lung nodules). The test set (90 scans; 5121 2D images) was used to assess if artificial images ...[more]

PMID: 37934344

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Dataset Information

Artificial CT images can enhance variation of case images in diagnostic radiology skills training.

Objectives

Methods

Results

Conclusion

Critical relevance statement

Key points

Publications

Artificial CT images can enhance variation of case images in diagnostic radiology skills training.

Similar Datasets

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