- Tytuł:
- Semantic segmentation and PSO based method for segmenting liver and lesion from CT images
- Autorzy:
-
Nayantara, Vaidehi P.
Surekha, Kamath
Manjunath, K.N.
Rajagopal, Kadavigere - Powiązania:
- https://bibliotekanauki.pl/articles/2146955.pdf
- Data publikacji:
- 2022
- Wydawca:
- Polska Akademia Nauk. Czytelnia Czasopism PAN
- Tematy:
-
liver lesion segmentation
computed tomography
semantic segmentation
SegNet
particle swarm optimization-based clustering
Hounsfield Unit - Opis:
- 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.
- Źródło:
-
International Journal of Electronics and Telecommunications; 2022, 68, 3; 635--640
2300-1933 - Pojawia się w:
- International Journal of Electronics and Telecommunications
- Dostawca treści:
- Biblioteka Nauki
Artykuł