PADDIT: Probabilistic Augmentation of Data using Diffeomorphic Image Transformation

学习可迁移的以对象为中心的微分同胚变换用于医学图像分割中的数据增强

Learning Transferable Object-Centric Diffeomorphic Transformations for Data Augmentation in Medical Image Segmentation

针对医学图像分割数据标注困难，提出以对象为中心的微分同胚变换增强模型，学习形状变化并原位增强，跨数据集迁移有效提升肾脏肿瘤分割。

Anatomical Data Augmentation via Fluid-based Image Registration

We introduce a fluid-based image augmentation method for medical image analysis. In contrast to existing methods, our framework generates anatomically meaningful images via interpolation from the geodesic subspace underlying given samples. Our approach consists of three steps: 1) given a source image and a set of target images, we construct a geodesic subspace using the Large Deformation Diffeomorphic Metric Mapping (LDDMM) model; 2) we sample transformations from the resulting geodesic subspace; 3) we obtain deformed images and segmentations via interpolation. Experiments on brain (LPBA) and …

Equitable modelling of brain imaging by counterfactual augmentation with morphologically constrained 3D deep generative models

We describe Countersynth, a conditional generative model of diffeomorphic deformations that induce label-driven, biologically plausible changes in volumetric brain images. The model is intended to synthesise counterfactual training data augmentations for downstream discriminative modelling tasks where fidelity is limited by data imbalance, distributional instability, confounding, or underspecification, and exhibits inequitable performance across distinct subpopulations. Focusing on demographic attributes, we evaluate the quality of synthesized counterfactuals with voxel-based morphometry, clas…

MGAug：图像形变潜在空间中的多模态几何增强

MGAug: Multimodal Geometric Augmentation in Latent Spaces of Image Deformations

本文提出MGAug模型，首次在微分同胚潜在空间中用高斯混合先验建模多模态几何变换，并通过VAE采样形变图像进行增强，在2D分类和3D脑MRI分割上优于现有方法。

Learning a Generative Motion Model from Image Sequences based on a Latent Motion Matrix

We propose to learn a probabilistic motion model from a sequence of images for spatio-temporal registration. Our model encodes motion in a low-dimensional probabilistic space - the motion matrix - which enables various motion analysis tasks such as simulation and interpolation of realistic motion patterns allowing for faster data acquisition and data augmentation. More precisely, the motion matrix allows to transport the recovered motion from one subject to another simulating for example a pathological motion in a healthy subject without the need for inter-subject registration. The method is b…

统一主动学习和半监督学习用于医学图像分割

Unifying Active Learning and Semi-Supervised Learning for Medical Image Segmentation

针对医学图像分割标注数据有限问题，提出RegAL统一框架，通过共享拓扑感知帕累托优化，结合样本获取与未标注数据利用，沿三个互补轴评估图像，在多数据集上于极端标注稀缺时表现稳定且优于基线。