FCSN: Global Context Aware Segmentation by Learning the Fourier Coefficients of Objects in Medical Images

Abstract

The encoder-decoder model is a commonly used Deep Neural Network (DNN) model for medical image segmentation. Conventional encoder-decoder models make pixel-wise predictions focusing heavily on local patterns around the pixel. This makes it challenging to give segmentation that preserves the object's shape and topology, which often requires an understanding of the global context. In this work, we propose a Fourier Coefficient Segmentation Network (FCSN)-a novel global context-aware DNN model that segments an object by learning the complex Fourier coefficients of the object's masks. The Fourier coefficients are calculated by integrating over the whole contour. Therefore, for our model to make a precise estimation of the coefficients, the model is motivated to incorporate the global context of the object, leading to a more accurate segmentation of the object's shape. This global context awareness also makes our model robust to unseen local perturbations during inference, such as additive noise or motion blur that are prevalent in medical images. We compare FCSN with other state-of-the-art global context-aware models (UNet++, DeepLabV3+, UNETR) on 5 medical image segmentation tasks, of which 3 are camera imaging datasets (ISIC_2018, RIM_CUP, RIM_DISC) and 2 are medical imaging datasets (PROSTATE, FE-TAL). When FCSN is compared with UNETR, FCSN attains significantly lower Hausdorff scores with 19.14 (6%), 17.42 (6%), 9.16 (14%), 11.18 (22%), and 5.98 (6%) for ISIC_2018, RIM_CUP, RIM_DISC, PROSTATE, and FETAL tasks respectively. Moreover, FCSN is lightweight by discarding the decoder module, which incurs significant computational overhead. FCSN only requires 29.7M parameters which are 75.6M and 9.9M fewer parameters than UNETR and DeepLabV3+, respectively. FCSN attains inference and training speeds of 1.6ms/img and 6.3ms/img, which is 8× and 3× faster than UNet and UNETR. The code for FCSN is made publicly available at https://github.com/nus-mornin-lab/FCSN.

A Comprehensive Study on Medical Image Segmentation using Deep Neural Networks

“…To address this, it is important to construct compact and portable models with a reduced number of parameters while still maintaining high performance for the intended task [152]. This can be achieved through various techniques such as regularization, model pruning, and efficient architectures 5) The "black box" nature of AI models: The "black box" nature of artificial intelligence raises important ethical, transparent, and explainability concerns in clinical medical applications.…”

Guest Editorial Artificial Intelligence-Driven Biomedical Imaging Systems for Precision Diagnostic Applications

“…The fourth paper by Jeon et al [7] proposes an innovative global context-aware DNN model that learns the complex Fourier coefficients of an object's masks to segment it. Integrating over the entire contour yields the Fourier coefficients.…”