PART Ⅰ: Development And Evaluation Of Deep Learning Based Segmentation Of Histologic Structures in The Kidney Cortex With Multiple Histologic Stains

Contact: Audrey Hu (Whatsapp:008613880143964) Email: audrey.hu@wecistanche.com

Catherine P.Jayapandian, Yiiang Chen,Andrew R. Janowczyk & et al.

INTRODUCTION

Renal biopsy interpretation remains the gold standard for the diagnosis and staging of native and transplant kidney diseases.Although visual morphologic assessment of the renal parenchyma may provide useful information for disease categorization, manual assessment and visual quantification by pathologists are time consuming and limited by poor intra and interreader reproducibility.7

The introduction of digital pathology in nephrology clinical trials has provided an unprecedented opportunity to test machine learning approaches for large-scale tissue quantification efforts. Standardization of pathology material acquisition has allowed worldwide consortia to establish digital pathology repositories containing thousands of digital renal biopsies for the evaluation of kidney diseases in adults and children,across diverse populations and pathology laboratories.4,10 This large-scale quantification, however, presents some new challenges. Unlike cancer pathology where hematoxylin and eosin(H&E)is generally the sole stain employed, renal biopsies require routine special stains such as Jones and periodic acid methenamine silver(SIL),periodic acid Schiff(PAS), and Masson trichrome(TRI).1,12Additionally, the multicenter nature of such consortia is reflected in the heterogeneity of preparations(e.g, integrity of tissue sections and quality of the stains.

Deep learning (DL) is a machine learning approach that recognizes patterns in images through a network of connected artificial neurons. DL uses deep convolutional neural networks(CNNs)that are capable of identifying patterns in complex histopathology data prone to such heterogeneity. U-Net is a popular semantic-based DL network validated in the context of biomedical image segmentation that takes spatial context of pixels into consideration as opposed to naive pixel-level DL classifiers.3 The output of U-Net is a high resolution image(typically the same size as the input image) with labeled class predictions at the pixel level.14-16

In this study, we evaluated the feasibility of DL approaches for automatic segmentation of 6 renal histologic primitives on 4 stains, using the digital renal biopsies from a multicenter Nephrotic Syndrome Study Network(NEPTUNE) dataset.2 In addition, we describe annotation and training considerations, specifically as they relate to DL algorithms for digital nephropathology. To the best of our knowledge, this is the largest comprehensive study to address applicability of DL approaches employable for kidney pathology images generated in a multicenter setting.

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RESULTS

DL performance per histologic primitive

Glomerular tuft. The classifer performed consistently across the 4 stains with only marginal differences in F-score and Dice similarity coefficient (DSC).A 5× digital magnification on PAS and H&E stains(Table l, Figures 1 and 2)resulted in optimal detection and segmentation.

Glomerular unit. Consistent quantitative performance metric with F-score and DSC over 0.89 were observed across all stains, with optimal results for detection and segmentation using 5×digital magnification on PAS and SIL stains(Table l, Figures l and 2).

Proximal tubular segments. Segmentation results varied little across the stains(F-score from 0.89 to 0.91, and DSC from 0.88 to 0.95), with PAS, SIL, and TRI stains having better performance than the H&E stain. A 10×magnification was optimal for detection and segmentation across all stains. (Table 1, Figures l and 3).

Distal tubular segments. Segmentation results were highly variable across all the stains: F-scores were 0.78 and 0.81 for H&E and TRI,respectively, and 0.91 and 0.93 for SIL and PAS, respectively. DSC scores were 0.78 and 0.82 for H&E and TRI, and 0.92 and 0.93 for SIL and PAS.Optimal results for detection and segmentation were obtained using 10× digital magnification on PAS and SIL stains (Table 1, Figures 1 and 3).

Arteries/arterioles. Artery/arteriole segmentation was variable across stains, with F-scores ranging from 0.79 to 0.85 across TRI, H&E,and PAS staining and DSCranging from 0.85 to 0.90. Optimal results for detection and segmentation were obtained using 10× on PAS stain (Table l, Figures 1 and 4).

Peritubular capillaries. Optimal results for detection and segmentation were obtained using 40×magnification on PAS stain(Table l, Figures 1 and 4).Qualitative segmentation results on the testing cohort show that most of the large sized peritubular capillaries were thin and long as they were cut tangentially from the biopsy. Although the size, shape, and textural presentation of peritubular capillaries varied (Figure 5a),the U-Net model was able to detect and segment peritubular capillaries of varying sizes and shapes(Figure 5). The classifier tends to perform better on thin and long,small to medium sized capillaries. However, capillaries with size less than 40 pixels （167 um²） failed to be identified or were inaccurately segmented.

Validation of DL models using nephrectomies. An F-score of 0.93 was obtained for 191 glomerular units,0.90 for 1484 proximal tubules, 0.93 for 1251 distal tubules,0.71 for 269 arteries/arterioles(Figure 6), and 0.90 for 3784 peritubular capillaries (Figure 7). The rare globally sclerotic glomeruli and atrophic tubules present in the sections were not segmented by the DL network.

DL segmentation performance across sites and artifacts. See Supplementary Figure S4.

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DL performance as a function of number of training exemplars

The rate of improvement of the network performance as a function of the number of training exemplars was observed to be different across histologic primitives. The number of exemplars needed to maximize network performance increases substantially from glomerular tufts to distal tubular segments, arteries/arterioles, and finally to peritubular caillaries (Figure 8).For larger structures such as glomerular tufts, it was observed that only 60 training samples were necessary to achieve an F-score of 0.89, with a 0.02 increase using 183 tufts. For smaller and largely represented structures such as distal tubules, a 0.07 increase in F-score was observed by increasing the number of exemplars from 507 to 2789.For structures such as arteries/arterioles with varying sizes, the F-score increased by 0.13, increasing the number of exemplars from 258 to 864.A significant increase in F-score from 0.27 to 0.81 was observed with peritubular capillaries by increasing the number of exemplars 2.5 times (i.e.,from 4273 to 10,975).

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Note: Cistanche is a traditional chinese tonic herb, it also been called the desert ginsng. cistanche is effective for renal function improvement. cistanche has the effects of nourish kidney and anti-renal disease.