Progressive and Coarse-to-Fine Network for Medical Image Registration Across Phases, Modalities and Patients

Wang, Sheng; Lv, Jinxin; Shi, Hongkuan; Wang, Yilang; Liang, Yuanhuai; Ouyang, Zihui; Wang, Zhiwei; Li, Qiang

doi:10.1007/978-3-030-97281-3_26

Cited by 1 publication

(1 citation statement)

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“…However, these methods ignored that it is difficult to accurately align the given two images at once, especially in the brain image with complex tissue structure. To address this, some methods (Hu et al 2019;Wang et al 2021a;Kang et al 2022;Zhao et al 2019c,b;Mok and Chung 2020;Wang et al 2021b;Lv et al 2022;Hu et al 2022) proposed to decompose the target deformation field by multi-scale CNNs or multiple cascaded CNNs models. Despite achieving good registration performance, such atlas-based segmentation methods are susceptible to tissue gray-scale blurring, resulting in inaccurate segmentation results, since they only rely on the similarity between images and lack guidance of the anatomical structures.…”

Section: Pointed Out That the Atlas-basedmentioning

confidence: 99%

Robust One-Shot Segmentation of Brain Tissues via Image-Aligned Style Transformation

Zeng

Wang

et al. 2023

AAAI

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One-shot segmentation of brain tissues is typically a dual-model iterative learning: a registration model (reg-model) warps a carefully-labeled atlas onto unlabeled images to initialize their pseudo masks for training a segmentation model (seg-model); the seg-model revises the pseudo masks to enhance the reg-model for a better warping in the next iteration. However, there is a key weakness in such dual-model iteration that the spatial misalignment inevitably caused by the reg-model could misguide the seg-model, which makes it converge on an inferior segmentation performance eventually. In this paper, we propose a novel image-aligned style transformation to reinforce the dual-model iterative learning for robust one-shot segmentation of brain tissues. Specifically, we first utilize the reg-model to warp the atlas onto an unlabeled image, and then employ the Fourier-based amplitude exchange with perturbation to transplant the style of the unlabeled image into the aligned atlas. This allows the subsequent seg-model to learn on the aligned and style-transferred copies of the atlas instead of unlabeled images, which naturally guarantees the correct spatial correspondence of an image-mask training pair, without sacrificing the diversity of intensity patterns carried by the unlabeled images. Furthermore, we introduce a feature-aware content consistency in addition to the image-level similarity to constrain the reg-model for a promising initialization, which avoids the collapse of image-aligned style transformation in the first iteration. Experimental results on two public datasets demonstrate 1) a competitive segmentation performance of our method compared to the fully-supervised method, and 2) a superior performance over other state-of-the-art with an increase of average Dice by up to 4.67%. The source code is available at: https://github.com/JinxLv/One-shot-segmentation-via-IST.

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