Subpixel object segmentation using wavelets and multi resolution analysis

TL;DR

This paper introduces a deep learning framework that uses wavelets and multi-resolution analysis for fast, accurate boundary prediction of 2D domains, outperforming traditional U-Net models in speed.

Contribution

A novel hybrid neural network architecture leveraging wavelets and MRA for efficient boundary segmentation with flexible priors on smoothness.

Findings

Up to 5x faster inference than U-Net

Achieves similar Dice score and Hausdorff distance as U-Net

Effective in delineating boundaries of medical images

Abstract

We propose a novel deep learning framework for fast prediction of boundaries of two-dimensional simply connected domains using wavelets and Multi Resolution Analysis (MRA). The boundaries are modelled as (piecewise) smooth closed curves using wavelets and the so-called Pyramid Algorithm. Our network architecture is a hybrid analog of the U-Net, where the down-sampling path is a two-dimensional encoder with learnable filters, and the upsampling path is a one-dimensional decoder, which builds curves up from low to high resolution levels. Any wavelet basis induced by a MRA can be used. This flexibility allows for incorporation of priors on the smoothness of curves. The effectiveness of the proposed method is demonstrated by delineating boundaries of simply connected domains (organs) in medical images using Debauches wavelets and comparing performance with a U-Net baseline. Our model demonstrates up to 5x faster inference speed compared to the U-Net, while maintaining similar performance in terms of Dice score and Hausdorff distance.