Information Geometric Approaches for Patient-Specific Test-Time Adaptation of Deep Learning Models for Semantic Segmentation.

in IEEE transactions on medical imaging by Hariharan Ravishankar, Naveen Paluru, Prasad Sudhakar, Phaneendra K Yalavarthy

TLDR

  • A novel framework using information geometric principles is proposed for test-time adaptation of deep-learning-based semantic segmentation models, achieving improved generalization and patient optimality without additional neural networks or anatomical prior information.

Abstract

The test-time adaptation (TTA) of deep-learning-based semantic segmentation models, specific to individual patient data, was addressed in this study. The existing TTA methods in medical imaging are often unconstrained, require anatomical prior information or additional neural networks built during training phase, making them less practical, and prone to performance deterioration. In this study, a novel framework based on information geometric principles was proposed to achieve generic, off-the-shelf, regularized patient-specific adaptation of models during test-time. By considering the pre-trained model and the adapted models as part of statistical neuromanifolds, test-time adaptation was treated as constrained functional regularization using information geometric measures, leading to improved generalization and patient optimality. The efficacy of the proposed approach was shown on three challenging problems: a) improving generalization of state-of-the-art models for segmenting COVID-19 anomalies in Computed Tomography (CT) images b) cross-institutional brain tumor segmentation from magnetic resonance (MR) images, c) segmentation of retinal layers in Optical Coherence Tomography (OCT) images. Further, it was demonstrated that robust patient-specific adaptation can be achieved without adding significant computational burden, making it first of its kind based on information geometric principles.

Overview

  • The study focuses on the test-time adaptation (TTA) of deep-learning-based semantic segmentation models for individual patient data.
  • A novel framework based on information geometric principles is proposed to achieve generic, off-the-shelf, regularized patient-specific adaptation during test-time.
  • The primary objective is to develop a method that improves generalization and patient optimality without requiring anatomical prior information or additional neural networks during the training phase.

Comparative Analysis & Findings

  • The proposed approach was shown to improve generalization in three challenging problems: COVID-19 anomaly segmentation in CT images, cross-institutional brain tumor segmentation from MR images, and retinal layer segmentation in OCT images.
  • The method uses information geometric measures to treat test-time adaptation as constrained functional regularization, leading to improved patient optimality.
  • The results demonstrate that robust patient-specific adaptation can be achieved without adding significant computational burden, making it the first method based on information geometric principles.

Implications and Future Directions

  • The proposed framework has the potential to improve the accuracy and generalizability of semantic segmentation models in medical imaging, leading to better patient outcomes.
  • Future studies can explore the application of this method to other medical imaging modalities and segmentation tasks.
  • The method can be further improved by incorporating domain adaptation techniques and exploring its integration with other TTA methods for even more robust performance.
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