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 implementationsLatest papers
H2ASeg: Hierarchical Adaptive Interaction and Weighting Network for Tumor Segmentation in PET/CT Images
However, modality-specific encoders used in these methods operate independently, inadequately leveraging the synergistic relationships inherent in PET and CT modalities, for example, the complementarity between semantics and structure.
Federated Modality-specific Encoders and Multimodal Anchors for Personalized Brain Tumor Segmentation
In practice, it is not uncommon that some FL participants only possess a subset of the complete imaging modalities, posing inter-modal heterogeneity as a challenge to effectively training a global model on all participants' data.
Attention-Enhanced Hybrid Feature Aggregation Network for 3D Brain Tumor Segmentation
In our approach, we utilized a multi-scale, attention-guided and hybrid U-Net-shaped model -- GLIMS -- to perform 3D brain tumor segmentation in three regions: Enhancing Tumor (ET), Tumor Core (TC), and Whole Tumor (WT).
SEDNet: Shallow Encoder-Decoder Network for Brain Tumor Segmentation
Despite the advancement in computational modeling towards brain tumor segmentation, of which several models have been developed, it is evident from the computational complexity of existing models which are still at an all-time high, that performance and efficiency under clinical application scenarios are limited.
CT Liver Segmentation via PVT-based Encoding and Refined Decoding
Accurate liver segmentation from CT scans is essential for effective diagnosis and treatment planning.
To deform or not: treatment-aware longitudinal registration for breast DCE-MRI during neoadjuvant chemotherapy via unsupervised keypoints detection
We use a clinical dataset with 1630 MRI scans from 314 patients treated with NAC.
Training and Comparison of nnU-Net and DeepMedic Methods for Autosegmentation of Pediatric Brain Tumors
External validation of the trained nnU-Net model on the multi-institutional BraTS-PEDs 2023 dataset revealed high generalization capability in segmentation of whole tumor and tumor core with Dice scores of 0. 87+/-0. 13 (0. 91) and 0. 83+/-0. 18 (0. 89), respectively.
Beyond Traditional Approaches: Multi-Task Network for Breast Ultrasound Diagnosis
Breast Ultrasound plays a vital role in cancer diagnosis as a non-invasive approach with cost-effective.
Brain Tumor Segmentation Based on Deep Learning, Attention Mechanisms, and Energy-Based Uncertainty Prediction
Brain tumors are one of the deadliest forms of cancer with a mortality rate of over 80%.
E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation
E2ENet achieves comparable accuracy on the large-scale challenge AMOS-CT, while saving over 68\% parameter count and 29\% FLOPs in the inference phase, compared with the previous best-performing method.