Dense multi-object 3D glomerular reconstruction and quantification on 2D serial section whole slide images

Deng, Ruining; Yang, Haichun; Asad, Zuhayr; Zhu, Zheyu; Wang, Shiru; Wheless, Lee; Fogo, Agnes B.; Huo, Yuankai

doi:10.1117/12.2611957

Cited by 4 publications

(5 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Accelerated+ Omni-seg pipeline is an advanced image segmentation framework that employs an efficient multi-label segmentation technique for pathology quantification. 11 The overall framework of the Accelerated+ Omni-seg pipeline is demonstrated in Fig. 3.…”

Section: Gpu Scenariomentioning

confidence: 99%

High-performance data management for whole slide image analysis in digital pathology

Leng,

Deng,

Bao

et al. 2024

Medical Imaging 2024: Digital and Computational Pathology

View full text Add to dashboard Cite

When dealing with giga-pixel digital pathology in whole-slide imaging, a notable proportion of data records holds relevance during each analysis operation. For instance, when deploying an image analysis algorithm on whole-slide images (WSI), the computational bottleneck often lies in the input-output (I/O) system. This is particularly notable as patch-level processing introduces a considerable I/O load onto the computer system. However, this data management process could be further paralleled, given the typical independence of patch-level image processes across different patches. This paper details our endeavors in tackling this data access challenge by implementing the Adaptable IO System version 2 (ADIOS2). Our focus has been constructing and releasing a digital pathology-centric pipeline using ADIOS2, which facilitates streamlined data management across WSIs. Additionally, we've developed strategies aimed at curtailing data retrieval times. The performance evaluation encompasses two key scenarios: (1) a pure CPU-based image analysis scenario ("CPU scenario"), and (2) a GPUbased deep learning framework scenario ("GPU scenario"). Our findings reveal noteworthy outcomes. Under the CPU scenario, ADIOS2 showcases an impressive two-fold speed-up compared to the brute-force approach. In the GPU scenario, its performance stands on par with the cutting-edge GPU I/O acceleration framework, NVIDIA Magnum IO GPU Direct Storage (GDS). From what we know, this appears to be among the initial instances, if any, of utilizing ADIOS2 within the field of digital pathology. The source code has been made publicly available at https://github.com/hrlblab/adios.

show abstract

Section: Gpu Scenariomentioning

confidence: 99%

High-performance data management for whole slide image analysis in digital pathology

Leng,

Deng,

Bao

et al. 2024

Medical Imaging 2024: Digital and Computational Pathology

View full text Add to dashboard Cite

show abstract

“…SAM capabilities to perform tumor segmentation, nontumor tissue segmentation, and cell nuclei segmentation are tested in Deng et al [16]. In it, the authors use the prompt types SAM accepts (one or more points, both positive and negative, and bounding boxes) in different combinations.…”

Section: Related Workmentioning

confidence: 99%

No More Training: SAM’s Zero-Shot Transfer Capabilities for Cost-Efficient Medical Image Segmentation

Gutiérrez,

Rodriguez-Echeverria,

Delgado

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Semantic segmentation of medical images presents an enormous potential for diagnosis and surgery. However, achieving precise results involves designing and training complex Deep Learning (DL) models specifically for this task, which is only available to some. Segment Anything Model (SAM) is a model developed by Meta capable of segmenting objects present in virtually any type of image. This paper showcases SAM's robustness and exceptional performance in medical image segmentation, even in the absence of direct training on these image types (lung Computed TomographyComputed Tomographies (CTs) and chest X-rays, in particular). Additionally, it achieves this impressive outcome while requiring minimal user intervention. Although the dataset used to train SAM does not contain a single sample of both medical image types, processing a popular dataset comprised of 20 volumes with a total of 3520 slices using the ViT-L version of the model yields an average Jaccard index of 91.45 % and an average Dice score of 94.95 %. The same version of the model achieves a 93.19 % Dice score and a 87.45 % Jaccard index when segmenting a frequently-used chest X-ray dataset. The values obtained are above the 70 % mark recommended in the literature, and close to state-of-the art models developed specifically for medical segmentation. These results are achieved without user interaction by providing the model with positive prompts based on the masks of the dataset used and a negative prompt located in the center of bounding box that contains the masks.

show abstract

“…Some works utilize staining‐agnostic approaches to achieve information transfer between different stainings, including virtual staining, 28 staining augmentation, 29 domain adaptation, 8,30,31 etc. Some researchers have investigated holistic analysis of individual glomeruli based on pathology image registration 32 . These have improved the generalization of the model for different stainings and reduced tedious tasks for renal pathologists.…”

Section: Introductionmentioning

confidence: 99%

“…Some researchers have investigated holistic analysis of individual glomeruli based on pathology image registration. 32 These have improved the generalization of the model for different stainings and reduced tedious tasks for renal pathologists. However, these methods lack matching for glomeruli, and still require manual location for glomerular matching at multiple levels, which cannot further improve diagnostic efficiency.…”

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

View full text Add to dashboard Cite

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.

show abstract

Dense multi-object 3D glomerular reconstruction and quantification on 2D serial section whole slide images

Cited by 4 publications

References 10 publications

High-performance data management for whole slide image analysis in digital pathology

High-performance data management for whole slide image analysis in digital pathology

No More Training: SAM’s Zero-Shot Transfer Capabilities for Cost-Efficient Medical Image Segmentation

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

Contact Info

Product

Resources

About