mlsanityData to 7 Oct 2026

3D Medical Image Segmentation based on multi-scale MPU-Net

arXiv 2307.05799 ↗First posted 11 Jul 2023eess.IV cs.CVcited by 1

Not on the map. Why?

Abstract (arXiv)

The high cure rate of cancer is inextricably linked to physicians' accuracy in diagnosis and treatment, therefore a model that can accomplish high-precision tumor segmentation has become a necessity in many applications of the medical industry. It can effectively lower the rate of misdiagnosis while considerably lessening the burden on clinicians. However, fully automated target organ segmentation is problematic due to the irregular stereo structure of 3D volume organs. As a basic model for this class of real applications, U-Net excels. It can learn certain global and local features, but still lacks the capacity to grasp spatial long-range relationships and contextual information at multiple scales. This paper proposes a tumor segmentation model MPU-Net for patient volume CT images, which is inspired by Transformer with a global attention mechanism. By combining image serialization with the Position Attention Module, the model attempts to comprehend deeper contextual dependencies and accomplish precise positioning. Each layer of the decoder is also equipped with a multi-scale module and a cross-attention mechanism. The capability of feature extraction and integration at different levels has been enhanced, and the hybrid loss function developed in this study can better exploit high-resolution characteristic information. Moreover, the suggested architecture is tested and evaluated on the Liver Tumor Segmentation Challenge 2017 (LiTS 2017) dataset. Compared with the benchmark model U-Net, MPU-Net shows excellent segmentation results. The dice, accuracy, precision, specificity, IOU, and MCC metrics for the best model segmentation results are 92.17%, 99.08%, 91.91%, 99.52%, 85.91%, and 91.74%, respectively. Outstanding indicators in various aspects illustrate the exceptional performance of this framework in automatic medical image segmentation.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 42 of its 112 references that are in the map. First posted on arXiv 11 Jul 2023.

Builds on

42 of 112 references.

References · 42

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  4. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  5. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  6. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  7. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  8. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  9. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  10. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,239
  11. 2015Fast R-CNNcited by 2,930
  12. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
30 more
  1. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,160
  2. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,141
  3. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,552
  4. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,518
  5. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,471
  6. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,420
  7. 2015Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)cited by 1,061
  8. 2015Effective Approaches to Attention-based Neural Machine Translationcited by 1,024
  9. 2018Dual Attention Network for Scene Segmentationcited by 1,001
  10. 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 954
  11. 2019The Liver Tumor Segmentation Benchmark (LiTS)cited by 504
  12. 2016R-FCN: Object Detection via Region-based Fully Convolutional Networkscited by 414
  13. 2017Residual Attention Network for Image Classificationcited by 385
  14. 2019A large annotated medical image dataset for the development and evaluation of segmentation algorithmscited by 371
  15. 2021Medical Transformer: Gated Axial-Attention for Medical Image Segmentationcited by 319
  16. 2019Learning Deep Transformer Models for Machine Translationcited by 309
  17. 2021TransBTS: Multimodal Brain Tumor Segmentation Using Transformercited by 265
  18. 2017Tversky loss function for image segmentation using 3D fully convolutional deep networkscited by 228
  19. 2016Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learningcited by 209
  20. 2018An overview of deep learning in medical imaging focusing on MRIcited by 140
  21. 2019An Attentive Survey of Attention Modelscited by 123
  22. 2016Medical Image Synthesis with Context-Aware Generative Adversarial Networkscited by 91
  23. 2018Tensor2Tensor for Neural Machine Translationcited by 85
  24. 2019Multi-scale self-guided attention for medical image segmentationcited by 84
  25. 2021U-Net Transformer: Self and Cross Attention for Medical Image Segmentationcited by 81
  26. 2017DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understandingcited by 71
  27. 2018HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentationcited by 66
  28. 2022PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functionscited by 62
  29. 2018Self-Attentional Acoustic Modelscited by 29
  30. 2021Pose Discrepancy Spatial Transformer Based Feature Disentangling for Partial Aspect Angles SAR Target Recognitioncited by 1

Built on by

Cited by year

Citing papers per year: 2023: 1.

1 paper.

  1. 2023Polyper: Boundary Sensitive Polyp Segmentationcited by 8