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

The liver is a vital organ of the human body andhepatic cancer is one of the major causes of cancer deaths. Earlyand rapid diagnosis can reduce the mortality rate. It can beachieved through computerized cancer diagnosis and surgeryplanning systems. Segmentation plays a major role in thesesystems. This work evaluated the efficacy of the SegNet model inliver and particle swarm optimization-based clustering techniquein liver lesion segmentation. The method was evaluated on portalvenous phase CT images obtained from ten patients at KasturbaHospital, Manipal. The segmentation results were satisfactory.The values for Dice Coefficient and volumetric overlap errorachieved were 0.940 ± 0.022 and 0.112 ± 0.038, respectively forliver and the results for lesion delineation were 0.4629 ± 0.287and 0.6986 ± 0.203, respectively. The proposed method is effectivefor liver segmentation. However, lesion segmentation needs to befurther improved for better accuracy

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

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