MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints

08/15/2019
by   Ouwen Huang, et al.
1

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 raw conventional delay-and-summed (DAS) beams into the approximate post-processed images found on clinical-grade scanners. Training MimickNet only requires post-processed image samples from a scanner of interest without the need for explicit pairing to raw DAS data. This flexibility allows it to hypothetically approximate any manufacturer's post-processing without access to the pre-processed data. MimickNet generates images with an average similarity index measurement (SSIM) of 0.930±0.0892 on a 300 cineloop test set, and it generalizes to cardiac cineloops outside of our train-test distribution achieving an SSIM of 0.967±0.002. We also explore the theoretical SSIM achievable by evaluating MimickNet performance when trained under gray-box constraints (i.e., when both pre-processed and post-processed images are available). To our knowledge, this is the first work to establish deep learning models that closely approximate current clinical-grade ultrasound post-processing under realistic black-box constraints where before and after post-processing data is unavailable. MimickNet serves as a clinical post-processing baseline for future works in ultrasound image formation to compare against. To this end, we have made the MimickNet software open source.

READ FULL TEXT

page 1

page 5

page 6

page 7

research
05/31/2018

Multiaccuracy: Black-Box Post-Processing for Fairness in Classification

Machine learning predictors are successfully deployed in applications ra...
research
07/09/2020

GAMA: a General Automated Machine learning Assistant

The General Automated Machine learning Assistant (GAMA) is a modular Aut...
research
11/25/2020

Post-Processed Posteriors for Banded Covariances

We consider Bayesian inference of banded covariance matrices and propose...
research
08/20/2018

Post-Processing of Word Representations via Variance Normalization and Dynamic Embedding

Although embedded vector representations of words offer impressive perfo...
research
11/24/2022

CycleGANWM: A CycleGAN watermarking method for ownership verification

Due to the proliferation and widespread use of deep neural networks (DNN...
research
08/03/2020

3D B-mode ultrasound speckle reduction using deep learning for 3D registration applications

Ultrasound (US) speckles are granular patterns which can impede image po...

Please sign up or login with your details

Forgot password? Click here to reset