ArticleComputers in biology and medicine2020
Tissue-specific and interpretable sub-segmentation of whole tumour burden on CT images by unsupervised fuzzy clustering.
Article in Computers in biology and medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 43 citations in OpenAlex.
- Review
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- Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential.Frontiers in oncology · 2022Review
- Early Prediction of Cerebral Computed Tomography under Intelligent Segmentation Algorithm Combined with Serological Indexes for Hematoma Enlargement after Intracerebral Hemorrhage.Computational and mathematical methods in medicine · 2022Article
- Clinically Interpretable Radiomics-Based Prediction of Histopathologic Response to Neoadjuvant Chemotherapy in High-Grade Serous Ovarian Carcinoma.Frontiers in oncology · 2022Article
- An automated liver segmentation in liver iron concentration map using fuzzy c-means clustering combined with anatomical landmark data.BMC medical imaging · 2021Article
- Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991-2020).Annals of operations research · 2021Article
- Automatic Liver Segmentation in CT Images with Enhanced GAN and Mask Region-Based CNN Architectures.BioMed research international · 2021Article
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Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundCancer typically exhibits genotypic and phenotypic heterogeneity, which can have prognostic significance and influence therapy response. Computed Tomography (CT)-based radiomic approaches calculate quantitative features of tumour heterogeneity at a mesoscopic level, regardless of macroscopic areas of hypo-dense (i.e., cystic/necrotic), hyper-dense (i.e., calcified), or intermediately dense (i.e., soft tissue) portions.
methodWith the goal of achieving the automated sub-segmentation of these three tissue types, we present here a two-stage computational framework based on unsupervised Fuzzy C-Means Clustering (FCM) techniques. No existing approach has specifically addressed this task so far. Our tissue-specific image sub-segmentation was tested on ovarian cancer (pelvic/ovarian and omental disease) and renal cell carcinoma CT datasets using both overlap-based and distance-based metrics for evaluation.
resultsOn all tested sub-segmentation tasks, our two-stage segmentation approach outperformed conventional segmentation techniques: fixed multi-thresholding, the Otsu method, and automatic cluster number selection heuristics for the K-means clustering algorithm. In addition, experiments showed that the integration of the spatial information into the FCM algorithm generally achieves more accurate segmentation results, whilst the kernelised FCM versions are not beneficial. The best spatial FCM configuration achieved average Dice similarity coefficient values starting from 81.94±4.76 and 83.43±3.81 for hyper-dense and hypo-dense components, respectively, for the investigated sub-segmentation tasks.
conclusionsThe proposed intelligent framework could be readily integrated into clinical research environments and provides robust tools for future radiomic biomarker validation.
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