แนวคิดหลัก
The proposed Inter-Intra-slice Interpolation Network (I3Net) efficiently processes medical images by fully exploring information from high in-plane resolution and compensating for low through-plane resolution, outperforming state-of-the-art methods.
บทคัดย่อ
The content discusses the problem of medical image synthesis, where CT and MR volumes often exhibit anisotropy with high in-plane resolution and low through-plane resolution. The authors propose an Inter-Intra-slice Interpolation Network (I3Net) to address this issue.
Key highlights:
The authors observe that performing slice-wise interpolation from the axial view can yield greater benefits than performing super-resolution from other views.
I3Net consists of an inter-slice branch and an intra-slice branch. The inter-slice branch utilizes PixelShuffle operations to supplement the limited information in low through-plane resolution from high in-plane resolution, enabling continual and diverse feature learning.
The intra-slice branch performs feature learning in the frequency domain, enforcing an equal learning opportunity for all frequency bands in a global context learning paradigm.
A cross-view block is proposed to take advantage of information from all three views (axial, coronal, and sagittal) in real-time without increasing too much computation.
Extensive experiments on public datasets demonstrate the effectiveness of I3Net, outperforming state-of-the-art super-resolution, video frame interpolation, and slice interpolation methods by a large margin.
สถิติ
The PSNR of I3Net exceeds all state-of-the-art methods by at least 1.14dB on the MSD dataset under the upscale factor of ×2.
คำพูด
"We are the first to look into a reasonable solution for medical slice synthesis by considering the nature of sparsely-sampled CT and MR (high in-plane resolution and low through-plane resolution)."
"We propose I3Net with an inter-slice branch and an intra-slice branch. The inter-slice branch compensates for the limited information available in low through-plane resolution by utilizing the high in-plane resolution. The intra-slice branch ensures equal learning across all frequency bands in a global learning context while improving structural and fine-grained information reconstruction."
"We verify the effectiveness of our method on Medical Segmentation Decathlon (MSD), KiTS19 and IXI datasets. The PSNR of our method exceeds all state-of-the-arts with at least 1.14dB improvement on MSD under the upscale factor of ×2."