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Shape Modeling of Longitudinal Medical Images: From Diffeomorphic Metric Mapping to Deep Learning
Computer Vision and Pattern Recognition · Submitted 27 Mar 2025
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim We discuss the synergistic combinations of existing technologies and potential directions for future research, underscoring key deficiencies in the current research landscape.
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The abstract, sentence by sentence
Living biological tissue is a complex system, constantly growing and changing in response to external and internal stimuli. These processes lead to remarkable and intricate changes in shape. Modeling and understanding both natural and pathological (or abnormal) changes in the shape of anatomical structures is highly relevant, with applications in diagnostic, prognostic, and therapeutic healthcare. Nevertheless, modeling the longitudinal shape change of biological tissue is a non-trivial task due to its inherent nonlinear nature. In this review, we highlight several existing methodologies and tools for modeling longitudinal shape change (i.e., spatiotemporal shape modeling). These methods range from diffeomorphic metric mapping to deep-learning based approaches (e.g., autoencoders, generative networks, recurrent neural networks, etc.). We discuss the synergistic combinations of existing technologies and potential directions for future research, underscoring key deficiencies in the current research landscape.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Living biological tissue is a complex system, constantly growing and changing in response to external and internal stimuli. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | These processes lead to remarkable and intricate changes in shape. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s2 | Modeling and understanding both natural and pathological (or abnormal) changes in the shape of anatomical structures is highly relevant, with applications in diagnostic, prognostic, and therapeutic healthcare. | background 1.00 | 0.04 | 0.06 | asserted 1.00 |
| s3 | Nevertheless, modeling the longitudinal shape change of biological tissue is a non-trivial task due to its inherent nonlinear nature. | background 0.79 | 0.06 | 0.04 | asserted 1.00 |
| s4 | In this review, we highlight several existing methodologies and tools for modeling longitudinal shape change (i.e., spatiotemporal shape modeling). | contribution 0.97 | 0.23 | 0.07 | asserted 0.99 |
| s5 | These methods range from diffeomorphic metric mapping to deep-learning based approaches (e.g., autoencoders, generative networks, recurrent neural networks, etc.). | contribution 0.55 | 0.05 | 0.06 | asserted 0.98 |
| s6 | We discuss the synergistic combinations of existing technologies and potential directions for future research, underscoring key deficiencies in the current research landscape. | future work 0.59 | 0.48 | 0.34 | asserted 0.85 |
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The paper
Original abstract
Living biological tissue is a complex system, constantly growing and changing in response to external and internal stimuli. These processes lead to remarkable and intricate changes in shape. Modeling and understanding both natural and pathological (or abnormal) changes in the shape of anatomical structures is highly relevant, with applications in diagnostic, prognostic, and therapeutic healthcare. Nevertheless, modeling the longitudinal shape change of biological tissue is a non-trivial task due to its inherent nonlinear nature. In this review, we highlight several existing methodologies and tools for modeling longitudinal shape change (i.e., spatiotemporal shape modeling). These methods range from diffeomorphic metric mapping to deep-learning based approaches (e.g., autoencoders, generative networks, recurrent neural networks, etc.). We discuss the synergistic combinations of existing technologies and potential directions for future research, underscoring key deficiencies in the current research landscape.
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How the literature uses it
What this paper does with the arXiv papers it cites
What it cites · 23 sentences without a confident label
Cites as support 1 paper
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DL techniques are notoriously data hungry, with larger dataset sizes contributing significantly towards improved efficacy of networks [ref] .
How much data is needed to train a medical image deep learning system to achieve necessary high accuracy? · Paucity of longitudinal datasets · 0.98
Cites as background 6 papers
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DMs consist of forward and reverse processes, wherein noise is added onto input data in successive steps and the resulting noise is reversed to reform the input data [ref] .
Denoising Diffusion Probabilistic Models · Future outlook and directions · 0.91
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Recurrent neural networks (RNNs) are a type of NN that are used to model sequential data such as a time series [ref] .
A Critical Review of Recurrent Neural Networks for Sequence Learning · Recurrent Neural Networks · 1.00
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Recurrent neural networks (RNNs) are a type of NN that are used to model sequential data such as a time series [ref] .
Recent Advances in Recurrent Neural Networks · Recurrent Neural Networks · 1.00
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