A Deep Attention-based U-Net for Airways Segmentation in Computed Tomography Images.

BACKGROUND: Airway segmentation is a way to quantify the diagnosis of pulmonary diseases, including chronic obstructive problems and bronchiectasis. Manual analysis by radiologists is a challenging task due to the complex airway structure. Additionally, tree-like patterns, varied shapes, sizes, and intensity make the manual airway segmentation task more complex. Deeper airways are even more difficult to segment as their intensity starts matching the lung parenchyma as the diameter of the airway cross-section gets reduced. OBJECTIVE: Many earlier works have proposed different deep learning networks for airway segmentation but were unable to achieve the desired performance; hence the task of airway segmentation still possesses challenges in this field. METHODS: This work proposes a convolutional neural network based on deep U-Net architecture and employs an attention block technique for airway segmentation. The attention mechanism aids in the extraction of the complicated and multi-sized airways found in the lung region, hence increasing the efficiency of the U-Net architecture. RESULTS: The model has been validated using VESSEL12 and EXACT09 datasets, individually and combined, with and without trachea images. The best DSC scores on EXACT09 and VESSEL12 datasets are 95.21% and 95.80%, respectively. The performance on both datasets combined gave a DSC score of 94.1%, showing that the overall performance of the proposed methodology is quite satisfactory. The generalizability of the model is also confirmed using k-fold cross-validation. The comparison of the proposed model to existing research on airway segmentation found competitive results. CONCLUSION: The use of an attention unit in the proposed model highlights the relevant information and reduces the irrelevant features, which helps to improve the performance and saves time.

Psoriasis Lesion Detection Using Hybrid Seeker Optimization-based Image Clustering.

BACKGROUND: In recent years, there has been a massive increase in the number of people suffering from psoriasis. For proper psoriasis diagnosis, psoriasis lesion segmentation is a prerequisite for quantifying the severity of this disease. However, segmentation of psoriatic lesions cannot be evaluated just by visual inspection as they exhibit inter and intra variability among the severity classes. Most of the approaches currently pursued by dermatologists are subjective in nature. The existing conventional clustering algorithm for objective segmentation of psoriasis lesion suffers from limitations of premature local convergence. OBJECTIVE: An alternative method for psoriatic lesion segmentation with objective analysis is sought in the present work. The present work aims at obtaining optimal lesion segmentation by adopting an evolutionary optimization technique that possesses a higher probability of global convergence for psoriasis lesion segmentation. METHODS: A hybrid evolutionary optimization technique based on the combination of two swarm intelligence algorithms, namely Artificial Bee Colony and Seeker Optimization algorithm, has been proposed. The initial population for the hybrid technique is obtained from the two conventional local- based approaches, i.e., Fuzzy C-means and K-means clustering algorithms. RESULTS: The initial population selection from the convergence of classical techniques reduces the effect of population dynamics on the final solution and hence yields precise lesion segmentation with a Jaccard Index of 0.91 from 720 psoriasis images. CONCLUSION: The performance comparison reflects the superior performance of the proposed algorithm over other swarm intelligence and conventional clustering algorithms.