Table 2 A summary of traditional methods...

Table 2

A summary of traditional methods for nuclei segmentation.

Ref.	Dataset	Methods	Pre-Processing	Post-Processing
[27]	20 slides of neuroblasts	Hysteresis Thresholding	Color space decomposition	Hole filling,smoothing, removalof false positives,watershed
[59]	30 cutaneous H&E stainedimages	Local Region Adaptive Thresholding	Hybrid Morphological Reconstructions	Opening
[6]	Real and synthetic microscopicimages	Tricalss Thresholding	–	–
[70]	Gold Standard Dataset	Hierarchical multilevel thresholding	Color deconvolution, opening	Dilation
[23]	20 leuokocyte images	Otsu’s threhsolding	Contrast stretching, histogram equalization	Closing
[102]	30 cytology pleural fluidimages	Otsu’s threhsolding	Median filtering, conversion into LAB color space	Opening
[60]	MoNuSeg	Adaptive Thresholding	Data Driven Color Transform	Convex hull algorithm, nuclei area priors based thresholding, hole filling
[61]	MoNuSeg	Local Modified Adaptive Thresholding, Self supervised classification for uncertain pixels	H component extraction, Data Driven Color Transform	Convex hull algorithm, morphological operations
[98]	19 H&E stained breast cancerimages	Radial Symmetry Transform, Marker Controlled Watershed	Color deconvolution, morphological filtering	Size and solidity based refinement, ellipse approximation
[97]	119 H&E breast,gastrointestinal,and Feulgenprostate images	Seeded watershed based on image driven markers	Gaussian Smoothing, Morphological Operations	Morphological Operations
[91]	52 DAB stained colorectal cancer images	Region growing based seeded watershed	Global and local thresholding for foreground extraction	Intensity based auto thresholding, ellipse fitting
[17]	Custom breast cancer H&E dataset	Skeleton model based marker controlled watershed	Color deconvolution, Otsu’s threshold ing, morphological operations	Size based false positive removal, morphological operations
[46]	34 fluorescence microscopy hepatocellular carcinomaimages	Iterative marker controlled watershed	Gradient map and distance transform of binary map	–
[76]	120 H&E breast cancer images	Circular Hough Transform based modified marker controlled watershed	Denoising, CLAHE, Morphological operations	–
[36]	H&E esophageal images	Improved active contour with growing energy	Iterative dual thresholding, ultimate erosion	–
[21]	100 H&E breast cancer images	Geodesic Active Contour	Expectation- Minimization algorithm	Overlap resolution
[20]	20 H&E breast cancer images	DoG filtering and thresholding followed by level set	Bilateral filtering, Gamma correction, morphological operations	–
[3]	MITOS dataset	Localized, Region Based Level Set	Stain normalization, color deconvolution, filtering	–
[78]	KMC, BreakHis datasets (breast cancer images)	Modified Chan-Vese Model using multi chan nel color data	Color normalization, color channel selection	Morphological operations, area based false positive removal
[45]	25 in-vivo and in vitro breast images	Multiscale LoG filter	Graph Cuts	Graph cuts with region adjacency coloring and alpha expansions
[18]	40 promyelo-cytic leukemia and 10 lung epithelial cell images	Distance Transform, Graph cuts	Maximum Likelihood Estimation	–
[107]	51 H&E cervical cell images	Adaptive and localized graph cuts	Conversion to HSV, V channel extraction, linear stretching, median filtering	Morphological and gradient features, concave points, and constrained ellipse fitting to split touching cells
[87]	Blood smear microscopic images	K-means clustering	Median filtering, LAB color space conversion	Erosion based region growing mechanism for nucleus splitting
[101]	35 pleural effusion cytology images	K-means clustering	Median filtering, CLAHE, LAB color space conversion	Distance transform based watershed, Ellipse fitting
[12]	Custom H&E tumor samples	K-means clustering	Feature extraction with Gabor filters	Elimination of false positives using cytological profile
[84]	45 synthetic cervical cytology images	Fuzzy C means clustering with spatial shape constraint	Complement-ing, histogram based binarization	False positive removal using area and shape priors, Closing

Ref.	Dataset	Methods	Pre-Processing	Post-Processing
[27]	20 slides of neuroblasts	Hysteresis Thresholding	Color space decomposition	Hole filling,smoothing, removalof false positives,watershed
[59]	30 cutaneous H&E stainedimages	Local Region Adaptive Thresholding	Hybrid Morphological Reconstructions	Opening
[6]	Real and synthetic microscopicimages	Tricalss Thresholding	–	–
[70]	Gold Standard Dataset	Hierarchical multilevel thresholding	Color deconvolution, opening	Dilation
[23]	20 leuokocyte images	Otsu’s threhsolding	Contrast stretching, histogram equalization	Closing
[102]	30 cytology pleural fluidimages	Otsu’s threhsolding	Median filtering, conversion into LAB color space	Opening
[60]	MoNuSeg	Adaptive Thresholding	Data Driven Color Transform	Convex hull algorithm, nuclei area priors based thresholding, hole filling
[61]	MoNuSeg	Local Modified Adaptive Thresholding, Self supervised classification for uncertain pixels	H component extraction, Data Driven Color Transform	Convex hull algorithm, morphological operations
[98]	19 H&E stained breast cancerimages	Radial Symmetry Transform, Marker Controlled Watershed	Color deconvolution, morphological filtering	Size and solidity based refinement, ellipse approximation
[97]	119 H&E breast,gastrointestinal,and Feulgenprostate images	Seeded watershed based on image driven markers	Gaussian Smoothing, Morphological Operations	Morphological Operations
[91]	52 DAB stained colorectal cancer images	Region growing based seeded watershed	Global and local thresholding for foreground extraction	Intensity based auto thresholding, ellipse fitting
[17]	Custom breast cancer H&E dataset	Skeleton model based marker controlled watershed	Color deconvolution, Otsu’s threshold ing, morphological operations	Size based false positive removal, morphological operations
[46]	34 fluorescence microscopy hepatocellular carcinomaimages	Iterative marker controlled watershed	Gradient map and distance transform of binary map	–
[76]	120 H&E breast cancer images	Circular Hough Transform based modified marker controlled watershed	Denoising, CLAHE, Morphological operations	–
[36]	H&E esophageal images	Improved active contour with growing energy	Iterative dual thresholding, ultimate erosion	–
[21]	100 H&E breast cancer images	Geodesic Active Contour	Expectation- Minimization algorithm	Overlap resolution
[20]	20 H&E breast cancer images	DoG filtering and thresholding followed by level set	Bilateral filtering, Gamma correction, morphological operations	–
[3]	MITOS dataset	Localized, Region Based Level Set	Stain normalization, color deconvolution, filtering	–
[78]	KMC, BreakHis datasets (breast cancer images)	Modified Chan-Vese Model using multi chan nel color data	Color normalization, color channel selection	Morphological operations, area based false positive removal
[45]	25 in-vivo and in vitro breast images	Multiscale LoG filter	Graph Cuts	Graph cuts with region adjacency coloring and alpha expansions
[18]	40 promyelo-cytic leukemia and 10 lung epithelial cell images	Distance Transform, Graph cuts	Maximum Likelihood Estimation	–
[107]	51 H&E cervical cell images	Adaptive and localized graph cuts	Conversion to HSV, V channel extraction, linear stretching, median filtering	Morphological and gradient features, concave points, and constrained ellipse fitting to split touching cells
[87]	Blood smear microscopic images	K-means clustering	Median filtering, LAB color space conversion	Erosion based region growing mechanism for nucleus splitting
[101]	35 pleural effusion cytology images	K-means clustering	Median filtering, CLAHE, LAB color space conversion	Distance transform based watershed, Ellipse fitting
[12]	Custom H&E tumor samples	K-means clustering	Feature extraction with Gabor filters	Elimination of false positives using cytological profile
[84]	45 synthetic cervical cytology images	Fuzzy C means clustering with spatial shape constraint	Complement-ing, histogram based binarization	False positive removal using area and shape priors, Closing

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