Recognition and Detection of Mitosis Event Based on Feature of Evolution in Time Domain

Chuang Chen; Wenwu Jia; Ya Wang

doi:10.3788/LOP56.241007

Laser & Optoelectronics Progress, Volume. 56, Issue 24, 241007(2019)

Recognition and Detection of Mitosis Event Based on Feature of Evolution in Time Domain

Chuang Chen, Wenwu Jia^*, and Ya Wang^**

Author Affiliations

School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China

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Figures & Tables(9)

Fig. 1. Processes of mitosis recognition and detection

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Fig. 2. Whole process of mitosis. It means that mitosis is completed when shape of “8” appears

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Fig. 3. Results of mitosis locations in phase-contrast microscopy image on C2C12 dataset. (a) Detection result of 918th frame; (b) detection result of 952nd frame; (c) detection result of 782nd frame; (d) detection result of 902nd frame

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Table 1. Experimental results with different levels on C2C12 dataset
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Table 1. Experimental results with different levels on C2C12 dataset
Method Accuracy /% Precision /% Recall /% F-score /%
Level-1 97.89 95.78 98.20 96.97
Level-2 97.95 98.14 95.91 96.97
Combine 98.30 97.81 97.27 97.51

Table 2. Experimental results with different pooling strategies on C2C12 dataset

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Table 2. Experimental results with different pooling strategies on C2C12 dataset

Pooling	Accuracy /%	Precision /%	Recall /%	F-score /%
Sum pooling	96.53	94.22	95.93	95.05
Max pooling	98.46	97.66	97.89	97.76
Gradient pooling 1	98.44	97.83	97.66	97.73
Gradient pooling 2	98.14	95.69	99.13	97.39
Max+Sum+G1	98.69	98.05	98.22	98.12
G1+G2	98.61	97.64	98.37	97.97
Max+Sum+G1+G2	98.30	97.82	97.27	97.51

Table 3. Experimental results with different features on C2C12 dataset
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Table 3. Experimental results with different features on C2C12 dataset
Feature Accuracy /% Precision /% Recall /% F-score /%
GIST 98.30 97.82 97.27 97.51
SIFT 98.27 97.34 97.25 97.29
CNN 98.34 96.34 99.04 97.67

Table 4. Experimental results with different features on C3H10 dataset
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Table 4. Experimental results with different features on C3H10 dataset
Feature Accuracy /% Precision /% Recall /% F-score /%
GIST 89.92 93.32 88.91 91.61
SIFT 88.96 93.53 88.96 91.18
CNN 95.77 94.70 89.40 94.95

Table 5. Comparison of EOF+SVM with other methods on C2C12 dataset
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Table 5. Comparison of EOF+SVM with other methods on C2C12 dataset
Method F-score /% Precision /% Recall /%
EOF+SVM 97.51 97.82 97.27
BoW+SVM 87.80 95.20 91.30
HCRF 91.80 91.97 92.50
HSCRF 93.12 92.40 93.70
HSCNF 93.50 92.40 94.60

Table 6. Comparison of EOF+SVM with other methods on C3H10 dataset
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Table 6. Comparison of EOF+SVM with other methods on C3H10 dataset
Method F-score /% Precision /% Recall /%
EOF+SVM 94.95 94.70 89.40
MM-HCRF+MM-SMM^[18] 91.80 95.80 88.10
MM-HCRF^[18] 87.20 82.80 92.20
EDCRF^[20] 88.90 91.30 87.00
CRF^[8] 81.50 90.50 75.30
HMM^[6] 81.00 83.40 79.40

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Chuang Chen, Wenwu Jia, Ya Wang. Recognition and Detection of Mitosis Event Based on Feature of Evolution in Time Domain[J]. Laser & Optoelectronics Progress, 2019, 56(24): 241007

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