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Found 10 papers, 2 papers with code
Analysis of the MICCAI Brain Tumor Segmentation -- Metastases (BraTS-METS) 2025 Lighthouse Challenge: Brain Metastasis Segmentation on Pre- and Post-treatment MRI
The BraTS-METS 2025 Lighthouse Challenge aims to address this critical need by establishing inter-rater and intra-rater variability in dataset annotation by generating high quality annotated datasets from four individual instances of segmentation by neuroradiologists while being recorded on video (two instances doing "from scratch" and two instances after AI pre-segmentation).
SynthFM: Training Modality-agnostic Foundation Models for Medical Image Segmentation without Real Medical Data
Foundation models like the Segment Anything Model (SAM) excel in zero-shot segmentation for natural images but struggle with medical image segmentation due to differences in texture, contrast, and noise.
QCResUNet: Joint Subject-level and Voxel-level Segmentation Quality Prediction
To validate the effectiveness of the proposed method, we conducted experiments on assessing its performance on evaluating the quality of two distinct segmentation tasks.
Is SAM 2 Better than SAM in Medical Image Segmentation?
Evaluating the performance of this new model in medical image segmentation, specifically in a zero-shot promptable manner, is crucial.
Framing image registration as a landmark detection problem for label-noise-aware task representation (HitR)
Accurate image registration is pivotal in biomedical image analysis, where selecting suitable registration algorithms demands careful consideration.
The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI
Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing.
Primitive Simultaneous Optimization of Similarity Metrics for Image Registration
Even though simultaneous optimization of similarity metrics is a standard procedure in the field of semantic segmentation, surprisingly, this is much less established for image registration.
MRI-based classification of IDH mutation and 1p/19q codeletion status of gliomas using a 2.5D hybrid multi-task convolutional neural network
A 2. 5D hybrid convolutional neural network was proposed to simultaneously localize the tumor and classify its molecular status by leveraging imaging features from MR scans and prior knowledge features from clinical records and tumor location.
Integrative Imaging Informatics for Cancer Research: Workflow Automation for Neuro-oncology (I3CR-WANO)
Mean Dice scores were 0. 882 ($\pm$0. 244) and 0. 977 ($\pm$0. 04) for whole tumor segmentation for WUSM and MDA, respectively.
The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients
Registration of longitudinal brain MRI scans containing pathologies is challenging due to dramatic changes in tissue appearance.
