Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
126 tokens/sec
GPT-4o
47 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Multi-target and multi-stage liver lesion segmentation and detection in multi-phase computed tomography scans (2404.11152v1)

Published 17 Apr 2024 in eess.IV and cs.CV

Abstract: Multi-phase computed tomography (CT) scans use contrast agents to highlight different anatomical structures within the body to improve the probability of identifying and detecting anatomical structures of interest and abnormalities such as liver lesions. Yet, detecting these lesions remains a challenging task as these lesions vary significantly in their size, shape, texture, and contrast with respect to surrounding tissue. Therefore, radiologists need to have an extensive experience to be able to identify and detect these lesions. Segmentation-based neural networks can assist radiologists with this task. Current state-of-the-art lesion segmentation networks use the encoder-decoder design paradigm based on the UNet architecture where the multi-phase CT scan volume is fed to the network as a multi-channel input. Although this approach utilizes information from all the phases and outperform single-phase segmentation networks, we demonstrate that their performance is not optimal and can be further improved by incorporating the learning from models trained on each single-phase individually. Our approach comprises three stages. The first stage identifies the regions within the liver where there might be lesions at three different scales (4, 8, and 16 mm). The second stage includes the main segmentation model trained using all the phases as well as a segmentation model trained on each of the phases individually. The third stage uses the multi-phase CT volumes together with the predictions from each of the segmentation models to generate the final segmentation map. Overall, our approach improves relative liver lesion segmentation performance by 1.6% while reducing performance variability across subjects by 8% when compared to the current state-of-the-art models.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (41)
  1. Recurrent residual u-net for medical image segmentation. Journal of Medical Imaging 6.
  2. Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39, 2481–2495.
  3. Intravenous contrast medium administration and scan timing at ct: considerations and approaches. Radiology 256, 32–61.
  4. Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features. Scientific data 4, 1–13.
  5. Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge. arXiv preprint arXiv:1811.02629 .
  6. The liver tumor segmentation benchmark (lits). Medical Image Analysis 84, 102680.
  7. Comparison of 3d segmentation algorithms for medical imaging, in: Twentieth IEEE International Symposium on Computer-Based Medical Systems (CBMS’07), IEEE. pp. 269–274.
  8. Octopusnet: a deep learning segmentation network for multi-modal medical images, in: Multiscale Multimodal Medical Imaging: First International Workshop, MMMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings 1, Springer. pp. 17–25.
  9. Volumetric segmentation via 3d active shape models, in: Proceedings Fifth IEEE Southwest Symposium on Image Analysis and Interpretation, IEEE. pp. 248–252.
  10. An image is worth 16x16 words: Transformers for image recognition at scale, in: International Conference on Learning Representations.
  11. Computed tomography versus magnetic resonance imaging for hepatic lesion characterization/diagnosis. Clinical Liver Disease 17, 159–164.
  12. Imaging evaluation of the liver in oncology patients: A comparison of techniques. World Journal of Hepatology 13, 1936.
  13. Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images, in: International MICCAI Brainlesion Workshop, Springer. pp. 272–284.
  14. Unetr: Transformers for 3d medical image segmentation, in: Proceedings of the IEEE/CVF winter conference on applications of computer vision, pp. 574–584.
  15. Deep residual learning for image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778.
  16. nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods 18, 203–211.
  17. Doubleu-net: A deep convolutional neural network for medical image segmentation, in: 2020 IEEE 33rd International symposium on computer-based medical systems (CBMS), IEEE. pp. 558–564.
  18. A comprehensive study on colorectal polyp segmentation with resunet++, conditional random field and test-time augmentation. IEEE journal of biomedical and health informatics 25, 2029–2040.
  19. A deep residual u-net convolutional neural network for automated lung segmentation in computed tomography images. Biocybernetics and Biomedical Engineering 40, 1314–1327.
  20. 3d segmentation techniques for medical volumes. Center for Visual Computing, Department of Computer Science, State University of New York 2000, 59–68.
  21. A fast algorithm for multilevel thresholding. J. Inf. Sci. Eng. 17, 713–727.
  22. Microsoft coco: Common objects in context, in: European conference on computer vision, Springer. pp. 740–755.
  23. Unseeded region growing for 3d image segmentation., in: ACM International Conference Proceeding Series, Citeseer. pp. 31–37.
  24. Swin transformer: Hierarchical vision transformer using shifted windows, in: Proceedings of the IEEE/CVF international conference on computer vision, pp. 10012–10022.
  25. A convnet for the 2020s, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 11976–11986.
  26. Fully convolutional networks for semantic segmentation, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3431–3440.
  27. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 .
  28. The multimodal brain tumor image segmentation benchmark (brats). IEEE transactions on medical imaging 34, 1993–2024.
  29. Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 .
  30. Liver cancer imaging: role of ct, mri, us and pet. Cancer imaging 4, S42.
  31. U-net: Convolutional networks for biomedical image segmentation, in: International Conference on Medical image computing and computer-assisted intervention, Springer. pp. 234–241.
  32. Mednext: transformer-driven scaling of convnets for medical image segmentation, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 405–415.
  33. Learning representations by back-propagating errors. nature 323, 533–536.
  34. Global burden of primary liver cancer in 2020 and predictions to 2040. Journal of Hepatology 77, 1598–1606.
  35. Segmentation of tumor tissue in gray medical images using watershed transformation method. Int. J. Adv. Comp. Techn. 2, 123–127.
  36. Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians 71, 209–249.
  37. Comparison of multiphase ct, fdg-pet and intra-operative ultrasound in patients with colorectal liver metastases selected for surgery. Annals of surgical oncology 14, 818–826.
  38. Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39, 1856–1867.
  39. Models genesis. Medical image analysis 67, 101840.
  40. Deeply-supervised cnn for prostate segmentation, in: 2017 international joint conference on neural networks, IEEE. pp. 178–184.
  41. Morphometric analysis of white matter lesions in mr images: method and validation. IEEE Transactions on Medical Imaging 13, 716–724.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now