CortexODE: Learning Cortical Surface Reconstruction by Neural ODEs

Ma, Qiang; Li, Liu; Kainz, Bernhard; Rueckert, Daniel; Alansary, Amir

doi:10.1109/tmi.2022.3206221

Cited by 14 publications

(7 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Considering the unique role the hippocampus plays in human neuroanatomy and cognition (Duvernoy et al, 2013;O'Keefe & Nadel, 1978) and its likely important computational properties (Knierim & Neunuebel, 2016;, it may furthermore provide key insights and guidance into the design and validation of emerging bio-inspired AI architectures. We anticipate that surface-based registration will become the standard for hippocampal mapping, as it has in the neocortex (Fischl, Sereno, Tootell, et al, 1999;Glasser et al, 2013;Ma et al, 2023;Robinson et al, 2014;D. C. Van Essen et al, 1998).…”

Section: Discussionmentioning

confidence: 99%

“…We anticipate that surface-based registration will become the standard for hippocampal mapping, as it has in the neocortex (Fischl, Sereno, Tootell, et al, 1999; Glasser et al, 2013; Ma et al, 2023; Robinson et al, 2014; D. C. Van Essen et al, 1998). HippoMaps is a major step in advancing the usability of this methodology, generating utilities, scientific context, and an open community for examining the hippocampus in detail.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

HippoMaps: Multiscale cartography of the human hippocampal formation

DeKraker,

Cabalo,

Royer

et al. 2024

Preprint

View full text Add to dashboard Cite

The hippocampus has a unique microarchitecture, is situated at the nexus of multiple macroscale functional networks, contributes to numerous cognitive as well as affective processes, and is highly susceptible to brain pathology across common disorders. These features make the hippocampus a model to understand how brain structure covaries with function, in both health and disease. Here, we introduce HippoMaps, an open access toolbox and online data warehouse for the mapping and contextualization of hippocampal data in the human brain (http://hippomaps.readthedocs.io). HippoMaps capitalizes on a novel hippocampal unfolding approach as well as shape intrinsic registration capabilities to allow for cross-subject and cross-modal data aggregation. We initialize this repository with data spanning 3D post-mortem histology, ex-vivo 9.4 Tesla MRI, as well as in-vivo structural MRI and resting-state functional MRI (rsfMRI) obtained at 3 and 7 Tesla, together with intracranial encephalography (iEEG) recordings in epilepsy patients. HippoMaps also contains validated tools for spatial map association analysis in the hippocampus that correct for autocorrelation. All code and data are compliant with community standards, and comprehensive online tutorials facilitate broad adoption. Applications of this work span methodologies and modalities, spatial scales, as well as clinical and basic research contexts, and we encourage community feedback and contributions in the spirit of open and iterative scientific resource development.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

HippoMaps: Multiscale cartography of the human hippocampal formation

DeKraker,

Cabalo,

Royer

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Motivated by the promising reconstruction results reported by the CortexFlow, Ma et al [51] have further proposed a NODE-based cortical surface reconstruction framework named CortexODE. The CortexODE comprises three steps.…”

Section: Applications In Medical Image Reconstructionmentioning

confidence: 99%

“…This theorem prevents intersections at the vertex of the surface. Ma et al [51] have proved that the CortexODE is Lipschitz continuous and have estimated the upper bound of the Lipschitz constant.…”

Section: Applications In Medical Image Reconstructionmentioning

confidence: 99%

See 1 more Smart Citation

On the Applications of Neural Ordinary Differential Equations in Medical Image Analysis

Niu,

Zhou,

Yan

et al. 2024

Preprint

View full text Add to dashboard Cite

Medical image analysis tasks are characterized by high-noise, volumetric, and multi-modality, posing challenges for the model that attempts to learn robust features from the input images. Over the last decade, deep neural networks (DNNs) have achieved enormous success in medical image analysis tasks, which can be attributed to their powerful feature representation capability. Despite the promising results reported in numerous literature, DNNs are also criticized for several pivotal limits, with one of the limitations is lack of safety. Safety plays an important role in the applications of DNNs during clinical practice, helping the model defend against potential attacks and preventing the model from silent failure prediction. The recently proposed Neural Ordinary Differential Equation (NODE), a continuous model bridging the gap between DNNs and ODE, provides a significant advantage in ensuring the model's safety. Among the variants of NODE, the neural memory ordinary differential equation (nmODE) owns the global attractor theoretically, exhibiting superiority in prompting the model's performance and robustness during applications. While NODE and its variants have been widely used in medical image analysis tasks, there is a lack of a comprehensive review of their applications, hindering the in-depth understanding of NODE's working principle and its potential applications. To mitigate this limitation, this paper thoroughly reviews the literature on the applications of NODE in medical image analysis from the following five aspects: segmentation, reconstruction, registration, disease prediction, and data generation. We also summarize both the strengths and downsides of the applications of NODE, followed by the possible research directions. To the best of our knowledge, this is the first review regards the applications of NODE in the field of medical image analysis. We hope this review can draw the researchers' attention to the great potential of NODE and its variants in medical image analysis.

show abstract