Intelligent Evaluation of Tumor Calcification Areas Based on Whole Slide Images

Zhenzhen Wan; Haocheng Li; Ning Shi; Yuwei Liu; Fang Liu

doi:10.3788/LOP240787

Laser & Optoelectronics Progress, Volume. 61, Issue 22, 2217001(2024)

Intelligent Evaluation of Tumor Calcification Areas Based on Whole Slide Images

Zhenzhen Wan¹, Haocheng Li¹, Ning Shi^1、*, Yuwei Liu¹, and Fang Liu^2、**

¹Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, Hebei , China

²Department of Pathology, Baoding Hospital of Beijing Children's Hospital, Capital Medical University, Baoding 071002, Hebei , China

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Figures & Tables(12)

Fig. 1. ECR-UNet network structure

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Fig. 2. CBAM module

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Fig. 3. ECA module

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Fig. 4. Flow chart of the outer contour extracted from WSI. (a) Tumor WSI ; (b) grayscale; (c) Gaussian filter ; (d) Canny edge detection; (e) closing operation and opening operation; (f) binary image of the outer contour of the WSI

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Fig. 5. Multiple data augmentation examples. (a) Original image; (b) random horizontal flip; (c) random vertical flip; (d) random　rotation; (e) center crop; (f) random color jitter; (g) random brightness contrast

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Fig. 6. Segmentation results of different networks in calcified regions

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Fig. 7. Segmentation results of the outer contour. (a) (c) (e) (g) Tumor WSI; (b) (d) (f) (h) outer contour

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Fig. 8. Comparison bar chart of calcified area and pathological image contour area with gold standard. (a)Calcified area; (b) pathological image contour area

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Fig. 9. Comparison bar chart of calcification proportion and gold standard

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Table 1. Performance metrics for ablation experiment

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Table 1. Performance metrics for ablation experiment

Data set	Dice	Accuracy	Recall	Precision	Jaccard
U-Net	82.20	98.39	78.09	92.76	72.71
R18-UNet	82.35	98.35	85.24	84.14	72.08
R34-UNet	85.74	98.61	85.19	90.01	77.04
CR-UNet	86.43	98.65	84.58	91.09	77.69
ER-UNet	85.97	98.68	86.90	88.23	77.13
CER-UNet	86.56	98.70	87.82	87.82	77.56
ECR-UNet	89.13	98.94	89.21	90.81	81.72

Table 2. Comparison of performance metrics for different segmentation networks

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Table 2. Comparison of performance metrics for different segmentation networks

Data set	Dice	Accuracy	Recall	Precision	Jaccard
FCN-8s	76.56	97.62	81.22	78.75	64.20
SegNet	79.32	97.95	84.82	78.87	67.70
LinkNet	79.69	98.25	77.58	88.55	68.96
PspNet	81.31	98.12	84.18	82.66	70.54
U-Net	82.20	98.39	78.09	92.76	72.71
ECR-UNet	89.13	98.94	89.21	90.81	81.72

Table 3. Calculation accuracy of calcified region area, outer contour area, and proportion of calcified region

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Table 3. Calculation accuracy of calcified region area, outer contour area, and proportion of calcified region

NO.	Calcification area	Outer contour area	Proportion
1	96.77	98.74	98.00
2	97.67	97.62	95.18
3	98.39	98.90	99.49
4	85.27	99.28	84.43
5	89.57	99.45	90.06
6	99.74	99.66	99.40
7	85.96	98.88	84.66
8	86.84	99.06	85.76
9	92.66	98.84	91.40
10	85.96	99.62	85.52
11	95.96	99.55	95.49

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Zhenzhen Wan, Haocheng Li, Ning Shi, Yuwei Liu, Fang Liu. Intelligent Evaluation of Tumor Calcification Areas Based on Whole Slide Images[J]. Laser & Optoelectronics Progress, 2024, 61(22): 2217001

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Paper Information

Category: Medical Optics and Biotechnology

Received: Mar. 1, 2024

Accepted: Mar. 25, 2024

Published Online: Nov. 19, 2024

The Author Email: Ning Shi (shiningzhongguo@126.com), Fang Liu (asasfang@163.com)

DOI:10.3788/LOP240787

CSTR:32186.14.LOP240787

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology