Mask Enhanced Deeply Supervised Prostate Cancer Detection on B-mode Micro-Ultrasound
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Abstract (arXiv)
Prostate cancer is a leading cause of cancer-related deaths among men. The recent development of high frequency, micro-ultrasound imaging offers improved resolution compared to conventional ultrasound and potentially a better ability to differentiate clinically significant cancer from normal tissue. However, the features of prostate cancer remain subtle, with ambiguous borders with normal tissue and large variations in appearance, making it challenging for both machine learning and humans to localize it on micro-ultrasound images. We propose a novel Mask Enhanced Deeply-supervised Micro-US network, termed MedMusNet, to automatically and more accurately segment prostate cancer to be used as potential targets for biopsy procedures. MedMusNet leverages predicted masks of prostate cancer to enforce the learned features layer-wisely within the network, reducing the influence of noise and improving overall consistency across frames. MedMusNet successfully detected 76% of clinically significant cancer with a Dice Similarity Coefficient of 0.365, significantly outperforming the baseline Swin-M2F in specificity and accuracy (Wilcoxon test, Bonferroni correction, p-value<0.05). While the lesion-level and patient-level analyses showed improved performance compared to human experts and different baseline, the improvements did not reach statistical significance, likely on account of the small cohort. We have presented a novel approach to automatically detect and segment clinically significant prostate cancer on B-mode micro-ultrasound images. Our MedMusNet model outperformed other models, surpassing even human experts. These preliminary results suggest the potential for aiding urologists in prostate cancer diagnosis via biopsy and treatment decision-making.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 17 of its 47 references that are in the map. First posted on arXiv 14 Dec 2024.
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17 of 47 references.
References · 17
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
- 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,611
- 2021Masked-attention Mask Transformer for Universal Image Segmentationcited by 2,351
- 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,476
- 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,186
- 2018Attention-based Deep Multiple Instance Learningcited by 1,095
- 2018Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labelscited by 1,059
- 2021VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learningcited by 1,057
- 2014Deeply-Supervised Netscited by 367
- 2016The Importance of Skip Connections in Biomedical Image Segmentationcited by 126
- 2021End-to-end Prostate Cancer Detection in bpMRI via 3D CNNs: Effects of Attention Mechanisms, Clinical Priori and Decoupled False Positive Reductioncited by 32
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- 2023MicroSegNet: A Deep Learning Approach for Prostate Segmentation on Micro-Ultrasound Imagescited by 25
- 2022ProstAttention-Net: A deep attention model for prostate cancer segmentation by aggressiveness in MRI scanscited by 14
- 2022Towards Confident Detection of Prostate Cancer using High Resolution Micro-ultrasoundcited by 9
- 2023TRUSformer: Improving Prostate Cancer Detection from Micro-Ultrasound Using Attention and Self-Supervisioncited by 8
- 2022Self-Supervised Learning with Limited Labeled Data for Prostate Cancer Detection in High Frequency Ultrasoundcited by 7
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