Table 4 A summary of fully supervised... | Emerald Publishing

Table 4

A summary of fully supervised nuclei segmentation methods.

Ref.	Dataset	Methods	Pre-Processing	Post-Processing
[52]	DSB2018, MoNuSeg	Region based Mask RCNN with Guided Anchor RPN	Resizing	Soft Non Maximum Suppression
[81]	DSB2018	Mask RCNN with ResNet-101 backbone	Pretrained with weights of COCO dataset	Clump identification followed by marker controlled watershed
[24]	CoNSeP, Kumar, TNBC, CPM-15, CPM-17, CRCHisto	HoVerNet: Three branch U-Net with horizontal and vertical distance maps to separate nuclei clusters	Patch extraction	Gradient based marker controlled watershed from distance map
[11]	Kidney dataset, TNBC, MoNuSeg	High resolution wide and deep transferred ASPPU-Net	Patch extraction, Data augmentation
[43]	MoNuSeg, TNBC, CryoNuSeg, BBBC039V1	Dense ResU-Net with residual connections of atrous blocks	Color Normalization, patch extraction, Data Augmentation
[83]	Post-NAT-BRCA, MoNuSeg	Cascaded U-Net framework (U-Net with weighted pixel loss followed by Vanilla U-Net with a soft Dice loss	Zero padding, patch extraction, weighted mask generation	Erosion, Dilation, Reconstruction
[28]	MoNuSeg	Enhanced lightweight U-Net with generalized Dice loss	Stain normalization, resizing, patch extraction, data augmentation	Opening
[16]	DSB2018, BBBC006v1, BBBC039, PanNuke	CPP-Net with Context Enhancement, Confidence Based Weighting and Shape Aware Perceptual Loss		Semantic segmentation decoder, NMS, Reassignment of pixels to correct categories
Ref.	Dataset	Methods	Pre-Processing	Post-Processing
[48]	Kumar dataset	Ternary CNN with boundary class	Color normalization, (boundary annotation, pixel mapping in training stage), patch extraction	Seed detection by thresholding followed by region growing using boundary class
[67]	224 H&E stained images of ganglion cells from pediatric intestine	Boundary Enhanced U-Net with two decoders	Downsampling, random cropping, data augmentation
[110]	MoNuSeg	Contour Aware Information Aggregation Network with information aggregation modules between two decoders	Stain normalization, data augmentation	Nuclei and contour outputs subtracted, connected component identification
[15]	Kumar, CPM-17	Boundary assisted Region Proposal Network	Stain normalization, normalization, data augmentation
[72]	HUSTS, MoNuSeg, CoNSeP, CPM-17	Region Enhanced multitask U-Net with auxiliary tasks of rough segmentation and contour extraction	Patch extraction, data augmentation	Marker controlled watershed
[90]	150 3D abdominal CT scans, 82 3D pancreatic CT scans	Attention Gated U-Net	Dowsampling, data augmentation	-
[50]	KMC Liver dataset, Kumar dataset	NeucliSegNet (U-Net based) with robust residual blocks in encoder, bottleneck block, attention decoder block	Resizing, patch extraction. No pretraining	-
[105]	ClusteredCell, MoNuSeg, CoNSeP, CPM-17	Gating Context Aware Pooling integrated modified U-Net	Resizing, patch extraction, data augmentation, ImageNet pretrained ResNet-34	-
[51]	DSB2018, MoNuSeg	U-Net based Convolutional Blur Attention Network	Patch extraction, training data generation, Biorthogonal wavelet denoising	-

Ref.	Dataset	Methods	Pre-Processing	Post-Processing
[52]	DSB2018, MoNuSeg	Region based Mask RCNN with Guided Anchor RPN	Resizing	Soft Non Maximum Suppression
[81]	DSB2018	Mask RCNN with ResNet-101 backbone	Pretrained with weights of COCO dataset	Clump identification followed by marker controlled watershed
[24]	CoNSeP, Kumar, TNBC, CPM-15, CPM-17, CRCHisto	HoVerNet: Three branch U-Net with horizontal and vertical distance maps to separate nuclei clusters	Patch extraction	Gradient based marker controlled watershed from distance map
[11]	Kidney dataset, TNBC, MoNuSeg	High resolution wide and deep transferred ASPPU-Net	Patch extraction, Data augmentation
[43]	MoNuSeg, TNBC, CryoNuSeg, BBBC039V1	Dense ResU-Net with residual connections of atrous blocks	Color Normalization, patch extraction, Data Augmentation
[83]	Post-NAT-BRCA, MoNuSeg	Cascaded U-Net framework (U-Net with weighted pixel loss followed by Vanilla U-Net with a soft Dice loss	Zero padding, patch extraction, weighted mask generation	Erosion, Dilation, Reconstruction
[28]	MoNuSeg	Enhanced lightweight U-Net with generalized Dice loss	Stain normalization, resizing, patch extraction, data augmentation	Opening
[16]	DSB2018, BBBC006v1, BBBC039, PanNuke	CPP-Net with Context Enhancement, Confidence Based Weighting and Shape Aware Perceptual Loss		Semantic segmentation decoder, NMS, Reassignment of pixels to correct categories
Ref.	Dataset	Methods	Pre-Processing	Post-Processing
[48]	Kumar dataset	Ternary CNN with boundary class	Color normalization, (boundary annotation, pixel mapping in training stage), patch extraction	Seed detection by thresholding followed by region growing using boundary class
[67]	224 H&E stained images of ganglion cells from pediatric intestine	Boundary Enhanced U-Net with two decoders	Downsampling, random cropping, data augmentation
[110]	MoNuSeg	Contour Aware Information Aggregation Network with information aggregation modules between two decoders	Stain normalization, data augmentation	Nuclei and contour outputs subtracted, connected component identification
[15]	Kumar, CPM-17	Boundary assisted Region Proposal Network	Stain normalization, normalization, data augmentation
[72]	HUSTS, MoNuSeg, CoNSeP, CPM-17	Region Enhanced multitask U-Net with auxiliary tasks of rough segmentation and contour extraction	Patch extraction, data augmentation	Marker controlled watershed
[90]	150 3D abdominal CT scans, 82 3D pancreatic CT scans	Attention Gated U-Net	Dowsampling, data augmentation	-
[50]	KMC Liver dataset, Kumar dataset	NeucliSegNet (U-Net based) with robust residual blocks in encoder, bottleneck block, attention decoder block	Resizing, patch extraction. No pretraining	-
[105]	ClusteredCell, MoNuSeg, CoNSeP, CPM-17	Gating Context Aware Pooling integrated modified U-Net	Resizing, patch extraction, data augmentation, ImageNet pretrained ResNet-34	-
[51]	DSB2018, MoNuSeg	U-Net based Convolutional Blur Attention Network	Patch extraction, training data generation, Biorthogonal wavelet denoising	-