Bone metastasis scintigram generation using generative adversarial learning with multi-receptive field learning and two-stage training

Qiang Lin, An Xie, Xianwu Zeng, Yongchun Cao, Zhengxing Man, Yusheng Hao, Caihong Liu, Xiaodi Huang

Research output: Contribution to journalArticlepeer-review

Abstract

Background
Deep learning is the primary method for conducting automated analysis of SPECT bone scintigrams. The lack of available large-scale data significantly hinders the development of well-performing deep learning models, as the performance of a deep learning model is positively correlated with the size of the dataset used. Therefore, there is an urgent demand for an automated data generation method to enlarge the dataset of SPECT bone scintigrams.

Purpose
We introduce a deep learning-based generation model that can generate realistic but not identical samples from the original SPECT bone scintigrams.

Methods
Following the generative adversarial learning architecture, a bone metastasis scintigram generation model christened BMS-Gen is proposed. First, BMS-Gen takes multiple input conditions and employs multi-receptive field learning to ensure that the generated samples are as realistic as possible. Second, BMS-Gen adopts generative adversarial learning to retain the diversity of the generated samples. Last, BMS-Gen uses a two-stage training strategy to improve the quality of the generated samples.

Results
Experimental evaluation conducted on a set of clinical data of SPECT BM scintigrams has shown the performance of the proposed BMS-Gen, achieving the best overall scores of 1678.0, 69.33, and 19.51 for FID (Fréchet Inception Distance), MSE (Mean Square Error), and PSNR (Peak Signal-to-Noise Ratio) metrics. The introduction of samples generated by BMS-Gen contributes a maximum (minimum) increase of 3.01% (0.15%) on the F-1 score and a maximum (minimum) increase of 6.83% (2.21%) on the DSC score for the image classification and segmentation tasks, respectively.

Conclusions
The proposed BMS-Gen model can be used as a promising tool for augmenting the data of bone scintigrams, greatly facilitating the development of deep learning-based automated analysis of SPECT bone scintigrams.
Original languageEnglish
Number of pages14
JournalMedical Physics
DOIs
Publication statusPublished - 03 Sept 2024

Fingerprint

Dive into the research topics of 'Bone metastasis scintigram generation using generative adversarial learning with multi-receptive field learning and two-stage training'. Together they form a unique fingerprint.

Cite this