TY - JOUR

T1 - AID-U-Net

T2 - An Innovative Deep Convolutional Architecture for Semantic Segmentation of Biomedical Images

AU - Tashk, Ashkan

AU - Herp, Jürgen

AU - Bjørsum-Meyer, Thomas

AU - Koulaouzidis, Anastasios

AU - Nadimi, Esmaeil S.

N1 - Publisher Copyright: © 2022 by the authors.

PY - 2022

Y1 - 2022

N2 - Semantic segmentation of biomedical images found its niche in screening and diagnostic applications. Recent methods based on deep learning convolutional neural networks have been very effective, since they are readily adaptive to biomedical applications and outperform other competitive segmentation methods. Inspired by the U-Net, we designed a deep learning network with an innovative architecture, hereafter referred to as AID-U-Net. Our network consists of direct contracting and expansive paths, as well as a distinguishing feature of containing sub-contracting and sub-expansive paths. The implementation results on seven totally different databases of medical images demonstrated that our proposed network outperforms the state-of-the-art solutions with no specific pre-trained backbones for both 2D and 3D biomedical image segmentation tasks. Furthermore, we showed that AID-U-Net dramatically reduces time inference and computational complexity in terms of the number of learnable parameters. The results further show that the proposed AID-U-Net can segment different medical objects, achieving an improved 2D F1-score and 3D mean BF-score of 3.82% and 2.99%, respectively.

AB - Semantic segmentation of biomedical images found its niche in screening and diagnostic applications. Recent methods based on deep learning convolutional neural networks have been very effective, since they are readily adaptive to biomedical applications and outperform other competitive segmentation methods. Inspired by the U-Net, we designed a deep learning network with an innovative architecture, hereafter referred to as AID-U-Net. Our network consists of direct contracting and expansive paths, as well as a distinguishing feature of containing sub-contracting and sub-expansive paths. The implementation results on seven totally different databases of medical images demonstrated that our proposed network outperforms the state-of-the-art solutions with no specific pre-trained backbones for both 2D and 3D biomedical image segmentation tasks. Furthermore, we showed that AID-U-Net dramatically reduces time inference and computational complexity in terms of the number of learnable parameters. The results further show that the proposed AID-U-Net can segment different medical objects, achieving an improved 2D F1-score and 3D mean BF-score of 3.82% and 2.99%, respectively.

KW - biomedical images

KW - convolutional neural networks

KW - semantic segmentation

KW - up and downsampling

U2 - 10.3390/diagnostics12122952

DO - 10.3390/diagnostics12122952

M3 - Journal article

C2 - 36552959

AN - SCOPUS:85144633021

VL - 12

JO - Diagnostics

JF - Diagnostics

SN - 2075-4418

IS - 12

M1 - 2952

ER -