ArticlePloS one2020
Deep learning based automatic segmentation of metastasis hotspots in thorax bone SPECT images.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence applied to musculoskeletal oncology: a systematic review.Skeletal radiology · 2022Pooled it
- The Role of Artificial Intelligence in Theranostics.Journal of nuclear medicine technology · 2025Review
- Metastasis lesion segmentation from bone scintigrams using encoder-decoder architecture model with multi-attention and multi-scale learning.Quantitative imaging in medicine and surgery · 2025Article
- Automated recognition and segmentation of lung cancer cytological images based on deep learning.PloS one · 2025Article
- Early diagnostic value of ECT whole-body bone imaging combined with PINP and β-CTX for bone metastasis of lung cancer.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2024Article
- Simultaneous segmentation and classification ofEJNMMI reports · 2024Article
- Artificial Intelligence in Detection, Management, and Prognosis of Bone Metastasis: A Systematic Review.Cancers · 2024Review
- FPNC Net: A hydrogenation catalyst image recognition algorithm based on deep learning.PloS one · 2024Article
- Bone Metastases Lesion Segmentation on Breast Cancer Bone Scan Images with Negative Sample Training.Diagnostics (Basel, Switzerland) · 2023Article
- The transformational potential of molecular radiomics.Journal of medical radiation sciences · 2023Review
- Review
- A tissue-fraction estimation-based segmentation method for quantitative dopamine transporter SPECT.Medical physics · 2022Article
- Semi-supervised segmentation of metastasis lesions in bone scan images.Frontiers in molecular biosciences · 2022Article
- Emerging Applications of Deep Learning in Bone Tumors: Current Advances and Challenges.Frontiers in oncology · 2022Review
- Multiclass classification of whole-body scintigraphic images using a self-defined convolutional neural network with attention modules.Medical physics · 2021Article
- Automatic identification of suspicious bone metastatic lesions in bone scintigraphy using convolutional neural network.BMC medical imaging · 2021Article
- A physics and learning-based transmission-less attenuation compensation method for SPECT.Proceedings of SPIE--the International Society for Optical Engineering · 2021Article
- Detection and Segmentation of Pelvic Bones Metastases in MRI Images for Patients With Prostate Cancer Based on Deep Learning.Frontiers in oncology · 2021Article
- Differentiation of Bone Metastasis in Elderly Patients With Lung Adenocarcinoma Using Multiple Machine Learning Algorithms.Cancer control : journal of the Moffitt Cancer CenterArticle
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7 authors.
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Abstract
SPECT imaging has been identified as an effective medical modality for diagnosis, treatment, evaluation and prevention of a range of serious diseases and medical conditions. Bone SPECT scan has the potential to provide more accurate assessment of disease stage and severity. Segmenting hotspot in bone SPECT images plays a crucial role to calculate metrics like tumor uptake and metabolic tumor burden. Deep learning techniques especially the convolutional neural networks have been widely exploited for reliable segmentation of hotspots or lesions, organs and tissues in the traditional structural medical images (i.e., CT and MRI) due to their ability of automatically learning the features from images in an optimal way. In order to segment hotspots in bone SPECT images for automatic assessment of metastasis, in this work, we develop several deep learning based segmentation models. Specifically, each original whole-body bone SPECT image is processed to extract the thorax area, followed by image mirror, translation and rotation operations, which augments the original dataset. We then build segmentation models based on two commonly-used famous deep networks including U-Net and Mask R-CNN by fine-tuning their structures. Experimental evaluation conducted on a group of real-world bone SEPCT images reveals that the built segmentation models are workable on identifying and segmenting hotspots of metastasis in bone SEPCT images, achieving a value of 0.9920, 0.7721, 0.6788 and 0.6103 for PA (accuracy), CPA (precision), Rec (recall) and IoU, respectively. Finally, we conclude that the deep learning technology have the huge potential to identify and segment hotspots in bone SPECT images.
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