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Visual Computing for Industry, Biomedicine, and Art
Fig. 4 | Visual Computing for Industry, Biomedicine, and Art

Fig. 4

From: PlaqueNet: deep learning enabled coronary artery plaque segmentation from coronary computed tomography angiography

Fig. 4

Comparison of four segmentation algorithms, each represented by a row, and the columns display the segmentation result plots. The first row corresponds to the FCN algorithm, which exhibited noticeable segmentation gaps. The second row represents the Deeplabv3 algorithm, which shown a significant over-segmentation in its results. The third row showcases the Deeplabv3plus algorithm, which demonstrates a lower degree of over-segmentation. The fourth row presents the PlaqueNet algorithm proposed in this study

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