MimickNet, Mimicking Clinical Image Post- Processing Under Black-Box Constraints
| Autor: | Mark L. Palmeri, Gregg E. Trahey, Ouwen Huang, Nick Bottenus, Will Long, Marcelo Lerendegui, Sina Farsiu |
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| Rok vydání: | 2020 |
| Předmět: |
Radiological and Ultrasound Technology
Phantoms Imaging Computer science Image quality business.industry Deep learning Ultrasound Contrast (statistics) Speckle noise 01 natural sciences Article 030218 nuclear medicine & medical imaging Computer Science Applications 03 medical and health sciences 0302 clinical medicine Black box 0103 physical sciences Image Processing Computer-Assisted Computer vision Artificial intelligence Electrical and Electronic Engineering business 010301 acoustics Software Ultrasonography |
| Zdroj: | IEEE Trans Med Imaging |
| ISSN: | 1558-254X 0278-0062 |
| DOI: | 10.1109/tmi.2020.2970867 |
| Popis: | Image post-processing is used in clinical-grade ultrasound scanners to improve image quality (e.g., reduce speckle noise and enhance contrast). These post-processing techniques vary across manufacturers and are generally kept proprietary, which presents a challenge for researchers looking to match current clinical-grade workflows. We introduce a deep learning framework, MimickNet, that transforms conventional delay-and-summed (DAS) beams into the approximate Dynamic Tissue Contrast Enhanced (DTCE™) post-processed images found on Siemens clinical-grade scanners. Training MimickNet only requires post-processed image samples from a scanner of interest without the need for explicit pairing to DAS data. This flexibility allows MimickNet to hypothetically approximate any manufacturer’s post-processing without access to the pre-processed data. MimickNet post-processing achieves a 0.940 ± 0.018 structural similarity index measurement (SSIM) compared to clinical-grade post-processing on a 400 cine-loop test set, 0.937 ± 0.025 SSIM on a prospectively acquired dataset, and 0.928 ± 0.003 SSIM on an out-of-distribution cardiac cine-loop after gain adjustment. To our knowledge, this is the first work to establish deep learning models that closely approximate ultrasound post-processing found in current medical practice. MimickNet serves as a clinical post-processing baseline for future works in ultrasound image formation to compare against. Additionally, it can be used as a pretrained model for fine-tuning towards different post-processing techniques. To this end, we have made the MimickNet software, phantom data, and permitted in vivo data open-source at https://github.com/ouwen/MimickNet . |
| Databáze: | OpenAIRE |
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