OBELISK-Net: Fewer layers to solve 3D multi-organ segmentation with sparse deformable convolutions

Medical Image Analysis
Mattias P HeinrichNassim Bouteldja

Abstract

Deep networks have set the state-of-the-art in most image analysis tasks by replacing handcrafted features with learned convolution filters within end-to-end trainable architectures. Still, the specifications of a convolutional network are subject to much manual design - the shape and size of the receptive field for convolutional operations is a very sensitive part that has to be tuned for different image analysis applications. 3D fully-convolutional multi-scale architectures with skip-connection that excel at semantic segmentation and landmark localisation have huge memory requirements and rely on large annotated datasets - an important limitation for wider adaptation in medical image analysis. We propose a novel and effective method based on trainable 3D convolution kernels that learns both filter coefficients and spatial filter offsets in a continuous space based on the principle of differentiable image interpolation first introduced for spatial transformer network. A deep network that incorporates this one binary extremely large and inflecting sparse kernel (OBELISK) filter requires fewer trainable parameters and less memory while achieving high quality results compared to fully-convolutional U-Net architectures on two chal...Continue Reading

Citations

Sep 11, 2020·Sensors·Satya P SinghBalázs Gulyás
Nov 8, 2020·Nature Communications·Oliver SchoppeBjoern H Menze
Mar 1, 2021·Medical Image Analysis·Cheng XuePheng-Ann Heng
Mar 16, 2021·Medical Image Analysis·Huisi WuJing Qin
May 17, 2021·Physica Medica : PM : an International Journal Devoted to the Applications of Physics to Medicine and Biology : Official Journal of the Italian Association of Biomedical Physics (AIFB)·Yabo FuXiaofeng Yang
May 23, 2021·Journal of Biomedical Informatics·Kumar T RajamaniMattias P Heinrich
Aug 3, 2021·Medical Image Analysis·Jianning LiJan Egger

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