Weakly supervised segmentation of tumor lesions in PET-CT hybrid imaging

Abstract Purpose A high expression of fibroblast activation protein (FAP) was observed in multiple sarcomas, indicating an enormous potential for PET/CT using 68Ga-radiolabeled inhibitors of FAP (FAPI). Therefore, this retrospective study aimed to evaluate the role of the novel hybrid imaging probe for sarcomas as a first clinical evaluation. Methods A cohort of 15 patients underwent 68Ga-FAPI-PET/CT for staging or restaging. The acquisition of PET scans was performed 60 min after administration of 127 to 308 MBq of the tracer. The uptake of 68Ga-FAPI in malignant tissue as well as in healthy organs was quantified by standardized uptake values SUVmean and SUVmax. Results Excellent tumor-to-background ratios (> 7) could be achieved due to low background activity and high SUVmax in primary tumors (median 7.16), local relapses (median 11.47), and metastases (median 6.29). The highest uptake was found for liposarcomas and high-grade disease (range 18.86–33.61). A high SUVmax (> 10) was observed for clinically more aggressive disease. Conclusion These preliminary findings suggest a high potential for the clinical use of 68Ga-FAPI-PET/CT for patients diagnosed with sarcoma.

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Bootstrapping Weakly Supervised Segmentation-free Word Spotting through HMM-based Alignment

2020 17th International Conference on Frontiers in Handwriting Recognition (ICFHR) ◽

10.1109/icfhr2020.2020.00020 ◽

2020 ◽

Author(s):

Tomas Wilkinson ◽

Carl Nettelblad

Keyword(s):

Word Spotting ◽

Supervised Segmentation ◽

Weakly Supervised ◽

Free Word

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Weakly supervised deep learning for determining the prognostic value of 18F-FDG PET/CT in extranodal natural killer/T cell lymphoma, nasal type

European Journal of Nuclear Medicine and Molecular Imaging ◽

10.1007/s00259-021-05232-3 ◽

2021 ◽

Author(s):

Rui Guo ◽

Xiaobin Hu ◽

Haoming Song ◽

Pengpeng Xu ◽

Haoping Xu ◽

...

Keyword(s):

Deep Learning ◽

Fdg Pet ◽

Cell Lymphoma ◽

Training Set ◽

Test Set ◽

Natural Killer T Cell ◽

Pet Ct ◽

Weakly Supervised ◽

Fdg Pet Ct ◽

Killer T Cell

Abstract Purpose To develop a weakly supervised deep learning (WSDL) method that could utilize incomplete/missing survival data to predict the prognosis of extranodal natural killer/T cell lymphoma, nasal type (ENKTL) based on pretreatment 18F-FDG PET/CT results. Methods One hundred and sixty-seven patients with ENKTL who underwent pretreatment 18F-FDG PET/CT were retrospectively collected. Eighty-four patients were followed up for at least 2 years (training set = 64, test set = 20). A WSDL method was developed to enable the integration of the remaining 83 patients with incomplete/missing follow-up information in the training set. To test generalization, these data were derived from three types of scanners. Prediction similarity index (PSI) was derived from deep learning features of images. Its discriminative ability was calculated and compared with that of a conventional deep learning (CDL) method. Univariate and multivariate analyses helped explore the significance of PSI and clinical features. Results PSI achieved area under the curve scores of 0.9858 and 0.9946 (training set) and 0.8750 and 0.7344 (test set) in the prediction of progression-free survival (PFS) with the WSDL and CDL methods, respectively. PSI threshold of 1.0 could significantly differentiate the prognosis. In the test set, WSDL and CDL achieved prediction sensitivity, specificity, and accuracy of 87.50% and 62.50%, 83.33% and 83.33%, and 85.00% and 75.00%, respectively. Multivariate analysis confirmed PSI to be an independent significant predictor of PFS in both the methods. Conclusion The WSDL-based framework was more effective for extracting 18F-FDG PET/CT features and predicting the prognosis of ENKTL than the CDL method.

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Optimal Scale of Hierarchical Image Segmentation with Scribbles Guidance for Weakly Supervised Semantic Segmentation

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001421540264 ◽

2021 ◽

pp. 2154026

Author(s):

Zaid Al-Huda ◽

Donghai Zhai ◽

Yan Yang ◽

Riyadh Nazar Ali Algburi

Keyword(s):

Image Segmentation ◽

Graphical Model ◽

Semantic Segmentation ◽

Saliency Map ◽

Training Data ◽

Deep Convolutional Neural Networks ◽

High Quality ◽

Optimal Scale ◽

Supervised Segmentation ◽

Weakly Supervised

Deep convolutional neural networks (DCNNs) trained on the pixel-level annotated images have achieved improvements in semantic segmentation. Due to the high cost of labeling training data, their applications may have great limitation. However, weakly supervised segmentation approaches can significantly reduce human labeling efforts. In this paper, we introduce a new framework to generate high-quality initial pixel-level annotations. By using a hierarchical image segmentation algorithm to predict the boundary map, we select the optimal scale of high-quality hierarchies. In the initialization step, scribble annotations and the saliency map are combined to construct a graphic model over the optimal scale segmentation. By solving the minimal cut problem, it can spread information from scribbles to unmarked regions. In the training process, the segmentation network is trained by using the initial pixel-level annotations. To iteratively optimize the segmentation, we use a graphical model to refine segmentation masks and retrain the segmentation network to get more precise pixel-level annotations. The experimental results on Pascal VOC 2012 dataset demonstrate that the proposed framework outperforms most of weakly supervised semantic segmentation methods and achieves the state-of-the-art performance, which is [Formula: see text] mIoU.

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