SAMRI fine-tunes only the mask decoder of SAM on 1.1 million MRI slices from 30 datasets to reach mean DSC 0.87 on 47 targets and strong zero-shot performance.
nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
abstract
The U-Net was presented in 2015. With its straight-forward and successful architecture it quickly evolved to a commonly used benchmark in medical image segmentation. The adaptation of the U-Net to novel problems, however, comprises several degrees of freedom regarding the exact architecture, preprocessing, training and inference. These choices are not independent of each other and substantially impact the overall performance. The present paper introduces the nnU-Net ('no-new-Net'), which refers to a robust and self-adapting framework on the basis of 2D and 3D vanilla U-Nets. We argue the strong case for taking away superfluous bells and whistles of many proposed network designs and instead focus on the remaining aspects that make out the performance and generalizability of a method. We evaluate the nnU-Net in the context of the Medical Segmentation Decathlon challenge, which measures segmentation performance in ten disciplines comprising distinct entities, image modalities, image geometries and dataset sizes, with no manual adjustments between datasets allowed. At the time of manuscript submission, nnU-Net achieves the highest mean dice scores across all classes and seven phase 1 tasks (except class 1 in BrainTumour) in the online leaderboard of the challenge.
representative citing papers
MAE-SAM2 integrates MAE self-supervised learning with SAM2 to achieve superior segmentation of retinal vascular leakage on fluorescein angiography images, with highest Dice/IoU scores and 5% improvement over original SAM2.
Self-adaptive 2D-3D FCN ensemble optimized by multiobjective evolution for prostate segmentation on PROMISE12 achieves top-10 ranking with smaller size than prior auto-designed models.
Dante is a new open-source backend for the Dafne ecosystem that implements configurable training from scratch, layer freezing, and channel-wise LoRA for medical image segmentation, with validation showing faster convergence and higher Dice scores in cross-domain MRI tasks.
citing papers explorer
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SAMRI: Segment Any MRI
SAMRI fine-tunes only the mask decoder of SAM on 1.1 million MRI slices from 30 datasets to reach mean DSC 0.87 on 47 targets and strong zero-shot performance.
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MAE-SAM2: Mask Autoencoder-Enhanced SAM2 for Clinical Retinal Vascular Leakage Segmentation
MAE-SAM2 integrates MAE self-supervised learning with SAM2 to achieve superior segmentation of retinal vascular leakage on fluorescein angiography images, with highest Dice/IoU scores and 5% improvement over original SAM2.
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Self-Adaptive 2D-3D Ensemble of Fully Convolutional Networks for Medical Image Segmentation
Self-adaptive 2D-3D FCN ensemble optimized by multiobjective evolution for prostate segmentation on PROMISE12 achieves top-10 ranking with smaller size than prior auto-designed models.
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Dante: An Open Source Model Pre-Training and Fine-Tuning Tool for the Dafne Federated Framework for Medical Image Segmentation
Dante is a new open-source backend for the Dafne ecosystem that implements configurable training from scratch, layer freezing, and channel-wise LoRA for medical image segmentation, with validation showing faster convergence and higher Dice scores in cross-domain MRI tasks.
- BrainDINO: A Brain MRI Foundation Model for Generalizable Clinical Representation Learning