Tumor Segmentation

221 papers with code • 3 benchmarks • 9 datasets

Tumor Segmentation is the task of identifying the spatial location of a tumor. It is a pixel-level prediction where each pixel is classified as a tumor or background. The most popular benchmark for this task is the BraTS dataset. The models are typically evaluated with the Dice Score metric.

Libraries

Use these libraries to find Tumor Segmentation models and implementations

Latest papers with no code

3D-TransUNet for Brain Metastases Segmentation in the BraTS2023 Challenge

no code yet • 23 Mar 2024

We identify that the Decoder-only 3D-TransUNet model should offer enhanced efficacy in the segmentation of brain metastases, as indicated by our 5-fold cross-validation on the training set.

Building Brain Tumor Segmentation Networks with User-Assisted Filter Estimation and Selection

no code yet • 19 Mar 2024

Brain tumor image segmentation is a challenging research topic in which deep-learning models have presented the best results.

Trustworthiness of Pretrained Transformers for Lung Cancer Segmentation

no code yet • 19 Mar 2024

We assessed the trustworthiness of two self-supervision pretrained transformer models, Swin UNETR and SMIT, for fine-tuned lung (LC) tumor segmentation using 670 CT and MRI scans.

D-Net: Dynamic Large Kernel with Dynamic Feature Fusion for Volumetric Medical Image Segmentation

no code yet • 15 Mar 2024

D-Net is able to effectively utilize a multi-scale large receptive field and adaptively harness global contextual information.

Advanced Tumor Segmentation in Medical Imaging: An Ensemble Approach for BraTS 2023 Adult Glioma and Pediatric Tumor Tasks

no code yet • 14 Mar 2024

This study outlines our methodology for segmenting tumors in the context of two distinct tasks from the BraTS 2023 challenge: Adult Glioma and Pediatric Tumors.

BraSyn 2023 challenge: Missing MRI synthesis and the effect of different learning objectives

no code yet • 12 Mar 2024

This work addresses the Brain Magnetic Resonance Image Synthesis for Tumor Segmentation (BraSyn) challenge, which was hosted as part of the Brain Tumor Segmentation (BraTS) challenge in 2023.

DALSA: Domain Adaptation for Supervised Learning From Sparsely Annotated MR Images

no code yet • 12 Mar 2024

We propose a new method that employs transfer learning techniques to effectively correct sampling selection errors introduced by sparse annotations during supervised learning for automated tumor segmentation.

A Segmentation Foundation Model for Diverse-type Tumors

no code yet • 11 Mar 2024

Large pre-trained models with their numerous model parameters and extensive training datasets have shown excellent performance in various tasks.

Modality-Aware and Shift Mixer for Multi-modal Brain Tumor Segmentation

no code yet • 4 Mar 2024

Combining images from multi-modalities is beneficial to explore various information in computer vision, especially in the medical domain.

Segment anything model for head and neck tumor segmentation with CT, PET and MRI multi-modality images

no code yet • 27 Feb 2024

Deep learning presents novel opportunities for the auto-segmentation of gross tumor volume (GTV) in head and neck cancer (HNC), yet fully automatic methods usually necessitate significant manual refinement.