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Background: Radiomics and deep learning have been widely investigated in the quantitative analysis of medical images. Deep learning radiomics (DLR), combining the strengths of both methods, is increasingly used in head and neck cancer (HNC). This systematic review was aimed at evaluating existing studies and assessing the potential application of DLR in HNC prognosis. Materials and methods: The PubMed, Embase, Scopus, Web of Science, and Cochrane databases were searched for articles published in the past 10 years with the keywords “radiomics,” “deep learning,” and “head and neck cancer” (and synonyms). Two independent reviewers searched, screened, and reviewed the English literature. The methodological quality of each article was evaluated with the Radiomics Quality Score (RQS). Data from the studies were extracted and collected in tables. A systematic review of radiomics prognostic prediction models for HNC incorporating deep learning techniques is presented. Result: A total of eight studies, published in 2012–2022, with a varying number of patients (59–707 cases), were included. Each study used deep learning; three studies performed automatic segmentation of regions of interest (ROI), and the Dice score range for automatic segmentation was 0.75–0.81. Four studies involved extraction of deep learning features, one study combined different modality features, and two studies performed predictive model building. The range of the area under the curve (AUC) was 0.84–0.96, the range of the concordance index (C-index) was 0.72–0.82, and the range of model accuracy (ACC) was 0.72–0.96. The median total RQS for these studies was 13 (10–15), corresponding to a percentage of 36.11% (27.78%–41.67). Low scores were due to a lack of prospective design, cost-effectiveness analysis, detection and discussion of biologically relevant factors, and external validation. Conclusion: DLR has potential to improve model performance in HNC prognosis.

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It is well known that froth visual features reflect the operating conditions of the flotation process, so that being able to accurately obtain the froth properties is the most significant criteria to optimize and control this process. Froth segmentation is a useful procedure that can determine the bubble size distribution. Several algorithms have been proposed in this field, but marker-based watershed transform shows the best performance. In spite of this fact, the algorithm suffers from oversegmentation in cases when the flotation froth includes large bubbles along with small ones. In the paper, the marker-based watershed method is modified to prevent oversegmentation of large bubbles. The developed algorithm is validated using some froth images captured at different operating conditions, so the results indicate that the method can segment the mixture of big and small bubbles effectively.

Introduction: In this study, the performance of computed tomography lung image segmentation using image processing and Markov Random Field was investigated. Before cancer segmentation and analysis, lung segmentation is an important initial process. Thus, the aim of this study is to find the optimal Markov Random Field setting for lung segmentation. Methods: The Centre for Diagnostic Nuclear Imaging at UPM provided 11 anonymous sets of cancerous lung CT images for this study. The thresholding technique is an effective method for medical image segmentation when the priori information for the region of interest is known, such as the Hounsfield Unit value of lung. Due to the large differences in grey levels in the image, the thresholding approach is difficult to apply in segmentation, especially for lung. Thus, for the segmentation process, this study used multilevel thresholding with Markov Random Field with three settings; Iterated Condition Mode, Metropolis algorithm, and Gibbs sampler. The images then went through image processing procedures which were binarization, small object removal, lung region extraction and lung segmentation. The output from the experiments were analyzed and compared to determine the ideal lung segmentation setting. Results: The Jaccard index average values; Markov Random Field -Metropolis = 0.9464, Markov Random Field -ICM = 0.9499 and Markov Random Field -Gibbs = 0.9512. The Dice index average values; Markov Random Field - Metropolis = 0.9743, Markov Random Field - ICM = 0.9724 and Markov Random Field - Gibbs = 0.9749. Conclusion: Markov Random Field using Gibbs sampler delivered the best results for lung segmentation.