mlsanityData to 7 Oct 2026

Brain Tumor Segmentation with Special Emphasis on the Non-Enhancing Brain Tumor Compartment

arXiv 2602.21703 ↗First posted 25 Feb 2026cs.CV cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

A U-Net based deep learning architecture is designed to segment brain tumors as they appear on various MRI modalities. Special emphasis is lent to the non-enhancing tumor compartment. The latter has not been considered anymore in recent brain tumor segmentation challenges like the MICCAI challenges. However, it is considered to be indicative of the survival time of the patient as well as of areas of further tumor growth. Hence it deems essential to have means to automatically delineate its extension within the tumor.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 22 of its 80 references that are in the map. First posted on arXiv 25 Feb 2026.

Builds on

22 of 80 references.

References · 22

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  2. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  3. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
  4. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,552
  5. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,420
  6. 2019UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentationcited by 583
  7. 2018Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challengecited by 508
  8. 2021The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classificationcited by 507
  9. 20183D MRI brain tumor segmentation using autoencoder regularizationcited by 324
  10. 2016Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentationcited by 263
  11. 2018Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentationcited by 180
  12. 2015Brain Tumor Segmentation with Deep Neural Networkscited by 137
10 more
  1. 2020nnU-Net for Brain Tumor Segmentationcited by 103
  2. 2018TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentationcited by 76
  3. 2021Common Limitations of Image Processing Metrics: A Picture Storycited by 58
  4. 2023Understanding metric-related pitfalls in image analysis validationcited by 57
  5. 2018Recent progress in semantic image segmentationcited by 39
  6. 2020Deep Learning Based Brain Tumor Segmentation: A Surveycited by 30
  7. 2019Effective and Efficient Dropout for Deep Convolutional Neural Networkscited by 21
  8. 2018A multi-scale pyramid of 3D fully convolutional networks for abdominal multi-organ segmentationcited by 17
  9. 2021Redundancy Reduction in Semantic Segmentation of 3D Brain Tumor MRIscited by 15
  10. 2020Robust Semantic Segmentation of Brain Tumor Regions from 3D MRIscited by 12

Built on by

Not cited yet.