Medical Image Segmentation Algorithm Based on Bilateral Fusion

Liming Liang; Jiang Yin; Yuanyuan Wu; Jun Feng

doi:10.3788/LOP202259.0817003

Journals >Laser & Optoelectronics Progress >Volume 59 >Issue 8 >Page 0817003 > Article

Laser & Optoelectronics Progress
Vol. 59, Issue 8, 0817003 (2022)

Medical Image Segmentation Algorithm Based on Bilateral Fusion

Liming Liang^*, Jiang Yin, Yuanyuan Wu, and Jun Feng

Author Affiliations

School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou , Jiangxi 341000, China

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DOI: 10.3788/LOP202259.0817003 Cite this Article Set citation alerts

Liming Liang, Jiang Yin, Yuanyuan Wu, Jun Feng. Medical Image Segmentation Algorithm Based on Bilateral Fusion[J]. Laser & Optoelectronics Progress, 2022, 59(8): 0817003 Copy Citation Text

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Fig. 1. Overall structure of medical image segmentation network model BFNet based on bilateral fusion

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Fig. 2. Structure of deep semantic fusion network

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Fig. 3. Structure of RFB

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Fig. 4. Structure of DFB

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Fig. 5. Structure of shallow detail fusion network

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Fig. 6. Structure of partition head

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Fig. 7. Comparison of visualization results of different algorithms on ISIC2017 dataset. (a) Original images; (b) manual labeling images by experts; (c) proposed algorithm; (d) Ref.[4]; (e) Ref.[7]; (f) Ref.[11]; (g) Ref.[27]

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Algorithm	mIoU /%	mDice /%	Precision /%	Recall /%	Overall accuracy /%
Ref.［4］	47.1	59.7	67.2	61.7	89.4
Ref.［5］	57.2	69.0	74.5	72.5	91.7
Ref.［10］	61.3	71.4	78.4	74.2	91.7
Ref.［3］	73.7	83.1	88.2	83.5	95.2
Ref.［12］	75.9	84.5	87.8	85.9	95.2
Ref.［23］	73.3	81.3	86.1	84.0	94.9
Ref.［24］	74.4	84.1	89.0	83.6	95.3
Ref.［25］	77.6	85.7	89.1	86.2	96.1
Ref.［25］	78.6	86.4	90.6	85.9	96.1
Ref.［26］	81.0	87.6	94.4	86.0	96.8
Ref.［20］	84.8	90.4	90.7	92.3	96.9
BFNet （ours）	86.2	92.3	89.1	86.7	96.1

Table 1. Comparison results of proposed algorithm and other algorithms on Kvasir-SEG dataset

Algorithm	mIoU /%	Overall accuracy /%	Kappa /%
Ref.［4］	89.11	93.32	87.34
Ref.［7］	89.19	95.33	88.46
Ref.［11］	89.53	95.48	88.46
Ref.［27］	89.64	95.50	89.96
BFNet （ours）	89.66	95.54	88.90

Table 2. Comparison results of proposed algorithm and other algorithms on ISIC2016 dataset

Algorithm	mIoU /%	Overall accuracy /%	Kappa /%
Ref.［4］	78.34	91.57	74.87
Ref.［7］	80.32	92.45	77.49
Ref.［11］	80.16	92.38	77.28
Ref.［27］	82.23	93.20	80.51
BFNet （ours）	83.41	93.39	81.45

Table 3. Comparison results of proposed algorithm and other algorithms on ISIC2017 dataset

Shallow network	Deep network			Agg	EL	mIoU /%	mDice /%	Overall accuracy /%
Shallow network	Backbone	RFB	DFB	Agg	EL	mIoU /%	mDice /%	Overall accuracy /%
√						79.81	88.30	92.00
	√	√	√			80.11	88.59	92.14
√	√			√	√	83.00	90.48	93.11
√	√	√		√	√	83.34	90.68	93.30
√	√	√	√	√		82.69	89.88	93.00
√	√	√	√	√	√	83.41	90.73	93.39

Table 4. Influence of different modules on network performance

Liming Liang, Jiang Yin, Yuanyuan Wu, Jun Feng. Medical Image Segmentation Algorithm Based on Bilateral Fusion[J]. Laser & Optoelectronics Progress, 2022, 59(8): 0817003

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