Application of Deep Learning in Automated Analysis of Molecular Images in Cancer: A Survey

Table 1

Comparison of the performance of different deep learning-based segmentation methods.

Publication	Type of images	Proposed methods		Comparison baseline
		Method	Results	Method	Results
Zhou et al. [14]	Multiple MRI	DNN	average = 0.864 (average of SEN, SPE and PRE)	Manifold learning	Average = 0.849
Zikic et al. [19]	BRAST 2013	CNN	HGG (complete): ACC = 0.837 ± 0.094	RF	HGG: ACC = 0.763 ± 0.124
Lyksborg et al. [20]	Multimodal MRI	CNN	Dice = 0.810, PPV = 0.833, SEN = 0.825	Axially trained 2D network	Dice = 0.744, PPV = 0.732, SEN = 0.811
Dvořák and Menze [23]	BRATS 2014	CNN	HGG (complete): Dice = 0.83 ± 0.13	—	—
Pereira et al. [24]	BRATS 2015	CNN	LGG (complete): DSC = 0.86, PPV = 0.86, SEN = 0.88; HGG (complete): DSC = 0.87, PPV = 0.89, SEN = 0.86; Combined: DSC = 0.87, PPV = 0.89, SEN = 0.86	—	—
Pereira et al. [25]	BRATS 2013	CNN	DSC = 0.88, PPV = 0.88, SEN = 0.89	Tumor growth model + tumor shape prior + EM	DSC = 0.88, PPV = 0.92, SEN = 0.84
Havaei et al. [27]	BRAST 2013	INPUTCASCADECNN	Dice = 0.88, SPE = 0.89, SEN = 0.87	RF	Dice = 0.87, SPE = 0.85, SEN = 0.89
Kamnitsas et al. [29]	BRATS 2015	Multiscale 3D CNN + CRF	DSC = 0.849,; PREC = 0.853,; SEN = 0.877	—	—
Yi et al. [32]	BRATS 2015	3D fully CNN	ACC = 0.89	GLISTR algorithm	ACC = 0.88
Casamitjana et al. [33]	BRATS 2015	Three different 3D fully connected CNNs	ACC = 0.9969/0.9971/0.9971	—	—
Zhao et al. [36]	BRATS 2013	3D fully CNN + CRF	Dice = 0.87,; PPV = 0.92,; SEN = 0.83	CNN	Dice = 0.88,; PPV = 0.88,; SEN = 0.89
Alex et al. [38]	BRATS 2013/2015	SDAE	ACC = 0.85 ± 0.04/0.73 ± 0.25	—	—
Ibragimov et al. [39]	CT, MR and PET images	CNN	Dice = 0.818	—	—

Notes. BRAST = multimodal brain tumor segmentation dataset, including four MRI sequences (T1W, T1-postcontrast (T1c), T2W, and FLAIR); CNN = convolutional neural networks; HGG = high-grade gliomas; ACC = accuracy; RF = random forests; DNN = deep neural network; Average = the average values of sensitivity, specificity, and precision; LGG = low-grade gliomas; PPV = positive predictive value; SEN = sensitivity; DSC = dice similarity coefficient; INPUTCASCADECNN = cascaded architecture using input concatenation; EM = expectation maximization algorithm; SPE = specificity; PREC = precision; GLISRT (glioma image segmentation and registration); CRF = conditional random fields; SDAE = stacked denoising autoencoder.