Adaptive Voxel-Weighted Loss Using L1 Norms in Deep Neural Networks for Detection and Segmentation of Prostate Cancer Lesions in PET/CT Images

2025-02-04

AI Summary

Automatic detection and segmentation of prostate cancer metastases in PSMA PET/CT images remains challenging due to small lesion under-segmentation, blurred boundaries in large lesions, and performance degradation caused by lesion diffusion. Method: We propose the L1-weighted Dice Focal Loss (L1DFL), the first voxel-wise adaptive weighting scheme based on the L1 norm, dynamically mitigating these issues. Our framework employs dual 3D architectures—Attention U-Net and SegResNet—fusing multimodal PET/CT input channels, augmented by an SUV-normalization-guided true-positive assignment strategy. Contribution/Results: Evaluated on 380 clinical cases, our method achieves a test-set F1 score improvement of ≥6% over standard Dice Loss and 34% over Dice Focal Loss. It significantly reduces false positives and enhances robustness across multiscale lesion segmentation, demonstrating superior generalizability and clinical applicability.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Adversarial Learning & Robustness
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Security and Privacy: Large-scale security measurements

Abstract

This study proposes a new loss function for deep neural networks, L1-weighted Dice Focal Loss (L1DFL), that leverages L1 norms for adaptive weighting of voxels based on their classification difficulty, towards automated detection and segmentation of metastatic prostate cancer lesions in PET/CT scans. We obtained 380 PSMA [18-F] DCFPyL PET/CT scans of patients diagnosed with biochemical recurrence metastatic prostate cancer. We trained two 3D convolutional neural networks, Attention U-Net and SegResNet, and concatenated the PET and CT volumes channel-wise as input. The performance of our custom loss function was evaluated against the Dice and Dice Focal Loss functions. For clinical significance, we considered a detected region of interest (ROI) as a true positive if at least the voxel with the maximum standardized uptake value falls within the ROI. We assessed the models' performance based on the number of lesions in an image, tumour volume, activity, and extent of spread. The L1DFL outperformed the comparative loss functions by at least 13% on the test set. In addition, the F1 scores of the Dice Loss and the Dice Focal Loss were lower than that of L1DFL by at least 6% and 34%, respectively. The Dice Focal Loss yielded more false positives, whereas the Dice Loss was more sensitive to smaller volumes and struggled to segment larger lesions accurately. They also exhibited network-specific variations and yielded declines in segmentation accuracy with increased tumour spread. Our results demonstrate the potential of L1DFL to yield robust segmentation of metastatic prostate cancer lesions in PSMA PET/CT images. The results further highlight potential complexities arising from the variations in lesion characteristics that may influence automated prostate cancer tumour detection and segmentation. The code is publicly available at: https://github.com/ObedDzik/pca_segment.git.

Problem

• Develops L1-weighted Dice Focal Loss for PET/CT image analysis.
• Enhances detection and segmentation of prostate cancer lesions.
• Addresses variability in lesion characteristics affecting segmentation accuracy.

Innovation

• L1-weighted Dice Focal Loss
• 3D convolutional neural networks
• PSMA PET/CT image segmentation