Computational Pipeline for Glomerular Segmentation and Association of the Quantified Regions with Prognosis of Kidney Function in IgA Nephropathy

Kawazoe, Yoshimasa; Shimamoto, Kiminori; Yamaguchi, Ryohei; Nakamura, Issei; Yoneda, Kota; Shinohara, Emiko; Shintani-Domoto, Yukako; Ushiku, Tetsuo; Tsukamoto, Tatsuo

doi:10.3390/diagnostics12122955

Cited by 5 publications

(4 citation statements)

References 41 publications

(76 reference statements)

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“…Tianyuan Yao et al developed and released a holistic Glo-In-One open-source toolkit to provide holistic glomerular detection, segmentation, and lesion characterization 14 . Kawazoe et al developed an automated computational pipeline for detecting glomeruli on PAS-stained WSIs, followed by segmenting Bowman's space, the glomerular tuft, the crescentic, and the sclerotic region inside of the glomeruli 15 . Silva et al proposed the end-to-end network, named DS-FNet, combining the strengths of semantic segmentation and semantic boundary detection networks via an attention-aware mechanism, and it showed consistently high performance in a one-to-many-stain glomerulus segmentation 16 .…”

Section: Deep Learning On Glomerular Identification and Classificationmentioning

confidence: 99%

An image inpainting-based data augmentation method for improved sclerosed glomerular identification performance with the segmentation model EfficientNetB3-Unet

He,

Zou,

et al. 2024

Sci Rep

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The percent global glomerulosclerosis is a key factor in determining the outcome of renal transfer surgery. At present, the rate is typically computed by pathologists, which is labour intensive and nonstandardized. With the development of Deep Learning (DL), DL-based segmentation models can be used to better identify and segment normal and sclerosed glomeruli. Based on this, we can better quantify percent global glomerulosclerosis to reduce the discard rate of donor kidneys. We used 51 whole slide images (WSIs) from different institutions that are publicly available on the internet. However, the number of sclerosed glomeruli is much smaller than that of normal glomeruli in different WSIs, which can reduce the effectiveness of Deep Learning. For better sclerosed glomerular identification and segmentation performance, we modified and trained a GAN (generative adversarial network)-based image inpainting model to obtain more synthetic sclerosed glomeruli. Our proposed inpainting method achieved an average SSIM (Structural Similarity) of 0.8086 and an average PSNR (Peak Signal-to-Noise Ratio) of 22.8943 dB in the area of generated sclerosed glomeruli. We obtained sclerosed glomerular segmentation performance improvement by adding synthetic sclerosed glomerular images and achieved the best Dice of glomerular segmentation in different test sets based on the modified Unet model.

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Section: Deep Learning On Glomerular Identification and Classificationmentioning

confidence: 99%

An image inpainting-based data augmentation method for improved sclerosed glomerular identification performance with the segmentation model EfficientNetB3-Unet

He,

Zou,

et al. 2024

Sci Rep

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“…The detection of renal glomeruli is fundamental for an accurate diagnosis and quantitative assessment of kidney pathology. 91 Kawazoe et al 92 developed an automated computing pipeline using the faster R-CNN object detection network, allowing the identification of glomeruli in periodic acid Schiff-stained WSI and further segmentation of pathological components within the glomeruli. This can enhance the efficiency of pathologists and enable the possibility of large-scale population-based studies.…”

Section: Application Of Object Detection Techniques In Other Non-live...mentioning

confidence: 99%

Object detection: A novel AI technology for the diagnosis of hepatocyte ballooning

Zheng,

Sha,

Deng

et al. 2023

Liver International

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Metabolic dysfunction‐associated fatty liver disease (MAFLD) has reached epidemic proportions worldwide and is the most frequent cause of chronic liver disease in developed countries. Within the spectrum of liver disease in MAFLD, steatohepatitis is a progressive form of liver disease and hepatocyte ballooning (HB) is a cardinal pathological feature of steatohepatitis. The accurate and reproducible diagnosis of HB is therefore critical for the early detection and treatment of steatohepatitis. Currently, a diagnosis of HB relies on pathological examination by expert pathologists, which may be a time‐consuming and subjective process. Hence, there has been interest in developing automated methods for diagnosing HB. This narrative review briefly discusses the development of artificial intelligence (AI) technology for diagnosing fatty liver disease pathology over the last 30 years and provides an overview of the current research status of AI algorithms for the identification of HB, including published articles on traditional machine learning algorithms and deep learning algorithms. This narrative review also provides a summary of object detection algorithms, including the principles, historical developments, and applications in the medical image analysis. The potential benefits of object detection algorithms for HB diagnosis (specifically those combined with a transformer architecture) are discussed, along with the future directions of object detection algorithms in HB diagnosis and the potential applications of generative AI on transformer architecture in this field. In conclusion, object detection algorithms have huge potential for the identification of HB and could make the diagnosis of MAFLD more accurate and efficient in the near future.

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“…Glomerular diseases are key information reflecting the etiology of CKD, so recognizing pathological manifestations is crucial 3,4 . Generally, the diameter of the adult glomerulus is about 100 microns, and the distance between two sections of biopsy tissue is about 3–5 microns.…”

Section: Introductionmentioning

confidence: 99%

“…Glomerular diseases are key information reflecting the etiology of CKD, so recognizing pathological manifestations is crucial. 3,4 Generally, the diameter of the adult glomerulus is about 100 microns, and the distance between two sections of biopsy tissue is about 3-5 microns. Since lesions can appear at any location, it is necessary to use several profiles to reflect richer information about the lesions.…”

mentioning

confidence: 99%

Identifying and matching 12‐level multistained glomeruli via deep learning for diagnosis of glomerular diseases

He,

Zeng,

et al. 2024

Int J Imaging Syst Tech

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The assessment of glomerular lesions is a fundamental step toward the diagnosis of glomerular diseases. This requires diagnosis and fusion of information from all the glomeruli at multiple levels and stainings. The lack of research on multi‐level multistained glomerular identification and matching has resulted in renal pathologists devoting much time and attention to this time‐consuming and labor‐intensive process. This limits the overall efficiency of the diagnosis of glomerular diseases. This paper constructed a dataset consisting of 600 cases, each containing 12 levels of whole slide images from H&E, PAS, Masson trichrome, and PASM staining. The glomeruli identifying and matching was proposed. First, a multistained transformer‐based Mask R‐CNN is proposed to extract the position and contours of the glomeruli. Second, coherent point drift‐based coarse matching and hybrid feature‐based fine matching achieve pairwise matching. Finally, the voting‐based cross‐matching realizes 12‐level multistained matching. This system constitutes a practical human‐computer interface. Intensive experiments were conducted to validate the ability to identify and match 12‐level multistained glomeruli. The mAP@50 for detection and segmentation reached 95.40% and 95.70%, respectively. The basic and comprehensive matching rates of the glomeruli matching reached 98.25% and 74.59%, respectively. Visualization results further demonstrate that the model achieved accurate identification and matching. The proposed system achieves accurate identification and matching of 12 levels of multistained glomeruli and can serve as a tool for pathological diagnosis of glomerular diseases by simplifying the diagnostic process. More importantly, this system can lay the foundation for the fully automated assisted diagnosis of glomerular diseases.

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Computational Pipeline for Glomerular Segmentation and Association of the Quantified Regions with Prognosis of Kidney Function in IgA Nephropathy

Cited by 5 publications

References 41 publications

An image inpainting-based data augmentation method for improved sclerosed glomerular identification performance with the segmentation model EfficientNetB3-Unet

An image inpainting-based data augmentation method for improved sclerosed glomerular identification performance with the segmentation model EfficientNetB3-Unet

Object detection: A novel AI technology for the diagnosis of hepatocyte ballooning

Identifying and matching 12‐level multistained glomeruli via deep learning for diagnosis of glomerular diseases

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