MODALITY-AGNOSTIC LEARNING FOR MEDICAL IMAGE SEGMENTATION USING MULTI-MODALITY SELF-DISTILLATION.

In medical image segmentation, although multi-modality training is possible, clinical translation is challenged by the limited availability of all image types for a given patient. Different from typical segmentation models, modality-agnostic (MAG) learning trains a single model based on all available modalities but remains input-agnostic, allowing a single model to produce accurate segmentation given any modality combinations. In this paper, we propose a novel frame-work, MAG learning through Multi-modality Self-distillation (MAG-MS), for medical image segmentation.

Enhancing Precision in Cardiac Segmentation for Magnetic Resonance-Guided Radiation Therapy Through Deep Learning.

Purpose: Cardiac substructure dose metrics are more strongly linked to late cardiac morbidities than to whole-heart metrics. Magnetic resonance (MR)-guided radiation therapy (MRgRT) enables substructure visualization during daily localization, allowing potential for enhanced cardiac sparing. We extend a publicly available state-of-the-art deep learning framework, "No New" U-Net, to incorporate self-distillation (nnU-Net.

Automated echocardiographic left ventricular dimension assessment in dogs using artificial intelligence: Development and validation.

Background: Artificial intelligence (AI) could improve accuracy and reproducibility of echocardiographic measurements in dogs.

Hypothesis: A neural network can be trained to measure echocardiographic left ventricular (LV) linear dimensions in dogs.

Animals: Training dataset: 1398 frames from 461 canine echocardiograms from a single specialist center.

MAGNET: A MODALITY-AGNOSTIC NETWORK FOR 3D MEDICAL IMAGE SEGMENTATION.

In this paper, we proposed MAGNET, a novel modality-agnostic network for 3D medical image segmentation. Different from existing learning methods, MAGNET is specifically designed to handle real medical situations where multiple modalities/sequences are available during model training, but fewer ones are available or used at time of clinical practice. Our results on multiple datasets show that MAGNET trained on multi-modality data has the unique ability to perform predictions using any subset of training imaging modalities.