Genetic algorithm for edge extraction of Glomerulus area

Zhang, Jun; Zhu, Hong; Qian, Xueming; Huang, Tao

doi:10.1109/icia.2004.1373383

Cited by 8 publications

(6 citation statements)

References 3 publications

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“…The majority of studies incorporate domain-specific morphometric or texture-based techniques to search for and define glomerular boundaries. Many of these have demonstrated boundary detection for small image patches containing isolated glomeruli [36]–[38], [41]–[43]. Translating detection techniques to whole-slide images (WSI) containing numerous glomeruli is a necessary but more difficult undertaking.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Global Glomerulosclerosis in Transplant Kidney Frozen Sections

Marsh

Matlock

Kudose

et al. 2018

IEEE Trans. Med. Imaging

129

107

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Transplantable kidneys are in very limited supply. Accurate viability assessment prior to transplantation could minimize organ discard. Rapid and accurate evaluation of intra-operative donor kidney biopsies is essential for determining which kidneys are eligible for transplantation. The criterion for accepting or rejecting donor kidneys relies heavily on pathologist determination of the percent of glomeruli (determined from a frozen section) that are normal and sclerotic. This percentage is a critical measurement that correlates with transplant outcome. Inter- and intra-observer variability in donor biopsy evaluation is, however, significant. An automated method for determination of percent global glomerulosclerosis could prove useful in decreasing evaluation variability, increasing throughput, and easing the burden on pathologists. Here, we describe the development of a deep learning model that identifies and classifies non-sclerosed and sclerosed glomeruli in whole-slide images of donor kidney frozen section biopsies. This model extends a convolutional neural network (CNN) pre-trained on a large database of digital images. The extended model, when trained on just 48 whole slide images, exhibits slide-level evaluation performance on par with expert renal pathologists. Encouragingly, the model's performance is robust to slide preparation artifacts associated with frozen section preparation. The model substantially outperforms a model trained on image patches of isolated glomeruli, in terms of both accuracy and speed. The methodology overcomes the technical challenge of applying a pretrained CNN bottleneck model to whole-slide image classification. The traditional patch-based approach, while exhibiting deceptively good performance classifying isolated patches, does not translate successfully to whole-slide image segmentation in this setting. As the first model reported that identifies and classifies normal and sclerotic glomeruli in frozen kidney sections, and thus the first model reported in the literature relevant to kidney transplantation, it may become an essential part of donor kidney biopsy evaluation in the clinical setting.

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Section: Related Workmentioning

confidence: 99%

Deep Learning Global Glomerulosclerosis in Transplant Kidney Frozen Sections

Marsh

Matlock

Kudose

et al. 2018

IEEE Trans. Med. Imaging

129

107

View full text Add to dashboard Cite

show abstract

“…The majority of studies incorporate domain-specific morphometric or texture-based techniques to search for and define glomerular boundaries. Many of these have demonstrated boundary detection for small image patches containing isolated glomeruli [31]- [33], [36]- [38]. Translating detection techniques to whole-slide images (WSI) containing numerous glomeruli is a necessary but more difficult undertaking.…”

Section: Related Workmentioning

confidence: 99%

“…The remainder describe differences in descriptors in glomeruli from normal and pathologic populations, rather than isolating and labeling different glomeruli within the same wide image field. The current study [27] WSI, anti-desmin, fixed rat segmentation (normal, diabetic) [28] field, anti-nestin, fixed rat segmentation, characterization (normal, renal failure) [23] WSI, PAS, fixed mouse segmentation (normal) [25] WSI, Jones H&E/PAS/other, fixed human segmentation (normal) [22] WSI, H&E/PAS/others human, mouse, rat segmentation, characterization (normal, diabetic) [29] WSI, H&E primate segmentation, characterization (normal, diseased) [30] WSI, Masson's trichrome human segmentation - [31] field, H&E mouse segmentation - [24] patch, PAS human segmentation - [32] patch rat segmentation, characterization (normal, hypertrophy) [33] patch, H&E/PAS, fixed human segmentation, characterization (normal, proliferating) [34], [35] patch, H&E/PAS, fixed mouse, rat segmentation, characterization (normal) [36]- [38] patch -segmentation demonstrates the novel use of CNNs applied to frozen H&E sections to detect non-sclerotic and sclerotic glomeruli to assist pathologists in intra-operative interpretation of percent global glomerulosclerosis.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Global Glomerulosclerosis in Transplant Kidney Frozen Sections

Marsh

Matlock

Kudose

et al. 2018

Preprint

View full text Add to dashboard Cite

Transplantable kidneys are in very limited supply. Accurate viability assessment prior to transplantation could minimize organ discard. Rapid and accurate evaluation of intraoperative donor kidney biopsies is essential for determining which kidneys are eligible for transplantation. The criteria for accepting or rejecting donor kidneys relies heavily on pathologist determination of the percent of glomeruli (determined from a frozen section) that are normal and sclerotic. This percentage is a critical measurement that correlates with transplant outcome. Inter-and intra-observer variability in donor biopsy evaluation is, however, significant. An automated method for determination of percent global glomerulosclerosis could prove useful in decreasing evaluation variability, increasing throughput, and easing the burden on pathologists. Here, we describe the development of a deep learning model that identifies and classifies non-sclerosed and sclerosed glomeruli in whole-slide images of donor kidney frozen section biopsies. This model extends a convolutional neural network (CNN) pre-trained on a large database of digital images. The extended model, when trained on just 48 whole slide images, exhibits slide-level evaluation performance on par with expert renal pathologists. The model substantially outperforms a model trained on image patches of isolated glomeruli. Encouragingly, the model's performance is robust to slide preparation artifacts associated with frozen section preparation. As the first model reported that identifies and classifies normal and sclerotic glomeruli in frozen kidney sections, and thus the first model reported in the literature relevant to kidney transplantation, it may become an essential part of donor kidney biopsy evaluation in the clinical setting.

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“…Xie and Ma [10] represented glomerulus shape accurately using the genetic algorithm that explored the optimal B-spline curve. Zhang et al [11] employed the genetic algorithm to extract edges in the glomerulus area. Zhang and Fan [12] developed a genetic algorithm based watershed transform for glomerulus extraction.…”

Section: Introductionmentioning

confidence: 99%