DyABD is the first benchmark dataset for abdominal muscle segmentation in dynamic MRIs featuring exercise-induced anatomical changes and pre/post-surgery scans, where existing models achieve an average Dice score of 0.82.
Nature Communications15(1), 654 (2024)
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
citation-role summary
baseline 1
citation-polarity summary
roles
baseline 1polarities
baseline 1representative citing papers
GenCellAgent deploys a planner-executor-evaluator LLM agent loop to automatically select, adapt, and refine segmentation tools for diverse cellular microscopy images, matching or exceeding specialist performance on 4,718 images across seven benchmarks while handling out-of-distribution and novel-ves
DualTrack uses decoupled local spatiotemporal and global anatomical encoders with a fusion module to estimate probe trajectories from 2D ultrasound sequences, achieving sub-5 mm average reconstruction error on public benchmarks.
FSAM integrates a frequency adapter into SAM with LoRA to extract domain-invariant high-frequency features and outperforms prior domain generalization methods on fundus and prostate datasets.
GleSAM++ improves SAM robustness on degraded images by using generative enhancement, feature alignment, and adaptive degradation prediction while adding few parameters.
A fine-tuned 3D foundation segmentation model combined with cross pseudo supervision achieves robust liver segmentation across labeled and unlabeled multi-phase, multi-vendor MRI without spatial registration.
citing papers explorer
-
DyABD: The Abdominal Muscle Segmentation in Dynamic MRI Benchmark
DyABD is the first benchmark dataset for abdominal muscle segmentation in dynamic MRIs featuring exercise-induced anatomical changes and pre/post-surgery scans, where existing models achieve an average Dice score of 0.82.
-
GenCellAgent: Generalizable, Training-Free Cellular Image Segmentation via Large Language Model Agents
GenCellAgent deploys a planner-executor-evaluator LLM agent loop to automatically select, adapt, and refine segmentation tools for diverse cellular microscopy images, matching or exceeding specialist performance on 4,718 images across seven benchmarks while handling out-of-distribution and novel-ves
-
DualTrack: Sensorless 3D Ultrasound needs Local and Global Context
DualTrack uses decoupled local spatiotemporal and global anatomical encoders with a fusion module to estimate probe trajectories from 2D ultrasound sequences, achieving sub-5 mm average reconstruction error on public benchmarks.
-
Frequency Adapter with SAM for Generalized Medical Image Segmentation
FSAM integrates a frequency adapter into SAM with LoRA to extract domain-invariant high-frequency features and outperforms prior domain generalization methods on fundus and prostate datasets.
-
Towards Any-Quality Image Segmentation via Generative and Adaptive Latent Space Enhancement
GleSAM++ improves SAM robustness on degraded images by using generative enhancement, feature alignment, and adaptive degradation prediction while adding few parameters.
-
Label-Efficient Cross-Modality Generalization for Liver Segmentation in Multi-Phase MRI
A fine-tuned 3D foundation segmentation model combined with cross pseudo supervision achieves robust liver segmentation across labeled and unlabeled multi-phase, multi-vendor MRI without spatial registration.