Semantic segmentation and PSO based method for segmenting liver and lesion from CT images
Abstract
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. The method was evaluated on portal
venous phase CT images obtained from ten patients at Kasturba
Hospital, Manipal. 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.
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