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03/25/2024

Pan-Cancer Nuclei Segmentation in Hematoxylin And Eosin Whole Slide Images

USCAP 2024 Annual Meeting PRESENTATION
Authors Mina Khoshdeli, Rohan Joshi, Bolesław L. Osinski, Luca Lonini, Martin C. Stumpe

Background

Nuclei segmentation is a critical stage in characterizing the morphology of cells in Hematoxylin and Eosin (H&E) stained whole slide images (WSIs). Extensive research has been conducted on the application of deep learning models for nuclei segmentation. While individual models have shown promising performance in segmenting nuclei for specific cancer types, a gap remains in the availability of a single model to segment nuclei across cancer types. Here, we created a comprehensive training and validation dataset encompassing a broad spectrum of cancer types and histological subtypes to address this need. 

Design

A cohort of WSIs was generated including 14 cancer types, with each having up to three histological subtypes (N=410, Table I). From these WSIs, we randomly selected two fields of view (FOV) from the tissue area for annotation at 40x magnification. During the training phase, we acquired one annotation per image, while for the validation phase, we gathered four annotations per image. We trained a HoverNet model initializing using weights from a model previously trained on the CoNSeP dataset. To assess the model performance, we conducted a comparative analysis between the trained model, the initial CoNSeP-trained model, and consensus evaluations of pathologists. To determine consensus among pathologists, we computed majority votes for all possible permutations of three pathologists, with one annotator being excluded for each permutation. We compared the consensus annotations with the model’s output and the annotations of the held-out pathologists. As a result, we obtained four data points for the box plots presented in Figure1 and 2. Statistical testing was performed using a t-test for independent samples and Bonferroni correction.

Results

We used a cell-based dice score to compare the performance of models and human annotators.  We found comparable performance between the model’s output and the performance of human annotators (mean 0.767, 95% CI 0.760-0.773 for the model; mean 0.794, 95% CI 0.777-0.810 for human annotators, p=0.08 with Bonferroni correction). In prostate, non-small cell lung, pancreatic, tumor of unknown origin, melanoma, gastric, bladder, and biliary cancers we found statistically indistinguishable performance in subgroup analyses.

Conclusions

In this study, we have established an annotation dataset tailored explicitly to pan-cancer nuclei segmentation and have demonstrated that by utilizing the HoverNet architecture, we have achieved near-human-level performance.

Table 1

Cancer Type  Histology subtypes Number of images in Training Number of images validation
Biliary Cancer cholangiocarcinoma 12 12
Bladder Cancer urothelial carcinoma 12 12
Breast Cancer breast carcinoma 10 11
Colorectal Cancer colorectal adenocarcinoma 12 12
Endometrial Cancer carcinosarcoma 12 12
endometrial serous carcinoma 12 12
endometrioid carcinoma 11 12
Esophageal Cancer gastroesophageal adenocarcinoma 12 12
gastroesophageal squamous cell carcinoma 12 12
gastroesophageal adenocarcinoma 12 12
Melanoma melanoma 12 12
Non-Small Cell Lung Cancer lung adenocarcinoma 12 12
lung squamous cell carcinoma 12 12
Ovarian Cancer ovarian serous carcinoma 12 11
Pancreatic Cancer pancreatic adenocarcinoma 12 12
pancreatic neuroendocrine tumor 10 12
Prostate Cancer prostatic adenocarcinoma 12 12
Sarcoma fibrous sarcoma 11 12
leiomyosarcoma 12 12
Tumor of Unknown Origin NA 36 36

Figure 1

Figure 2

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