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Semantic segmentation and PSO based method for segmenting liver and lesion from CT images

Nayantara Vaidehi P., Surekha Kamath, Manjunath K. N., Rajagopal Kadavigere

International Journal of Electronics and Telecommunications

2022

Vol. 68. No. 3

635--640

The liver is a vital organ of the human body and hepatic cancer is one of the major causes of cancer deaths. Early and rapid diagnosis can reduce the mortality rate. It can be achieved through computerized cancer diagnosis and surgery planning systems. Segmentation plays a major role in these systems. This work evaluated the efficacy of the SegNet model in liver and particle swarm optimization-based clustering technique in liver lesion segmentation. Over 2400 CT images were used for training the deep learning network and ten CT datasets for validating the algorithm. The segmentation results were satisfactory. The values for Dice Coefficient and volumetric overlap error achieved were 0.940 ± 0.022 and 0.112 ± 0.038, respectively for liver and the results for lesion delineation were 0.4629 ± 0.287 and 0.6986 ± 0.203, respectively. The proposed method is effective for liver segmentation. However, lesion segmentation needs to be further improved for better accuracy.

Deep-segmentation of plantar pressure images incorporating fully convolutional neural networks

Wang Dan, Li Zairan, Dey Nilanjan, Ashour Amira S., Moraru Luminita, Simon Sherratt R., Shi Fuqian

Biocybernetics and Biomedical Engineering

2020

Vol. 40, no. 1

546--558

Comfort shoe-last design relies on the key points of last curvature. Traditional plantar pressure image segmentation methods are limited to their local and global minimization issues. In this work, an improved fully convolutional networks (FCN) employing SegNet (SegNet+FCN 8 s) is proposed. The algorithm design and operation are performed using the visual geometry group (VGG). The method has high efficiency for the segmentation in positive indices of global accuracy (0.8105), average accuracy (0.8015), and negative indices of average cross-ratio (0.6110) and boundary F1 index (0.6200). The research has potential applications in improving the comfort of shoes.