Dr. Sina Salsabili Profile

Dr. Sina Salsabili

at Carleton Univ

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Publications (2)

SPIE Journal Paper | 9 May 2024

Multiresolution semantic segmentation of biological structures in digital histopathology

Sina Salsabili, Adrian D. Chan, Eranga Ukwatta

JMI, Vol. 11, Issue 03, 037501, (May 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.3.037501

KEYWORDS: Image segmentation, Semantics, Histopathology, Education and training, Lung, Blood vessels, Spatial resolution, Image resolution, Tissues, Performance modeling

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SPIE Journal Paper | 4 March 2021

Fully automated estimation of the mean linear intercept in histopathology images of mouse lung tissue

Marissa Lithopoulos, Sina Salsabili, Shreyas Sreeraman, Arul Vadivel, Bernard Thébaud, Adrian D. Chan, Eranga Ukwatta

JMI, Vol. 8, Issue 02, 027501, (March 2021) https://doi.org/10.1117/12.10.1117/1.JMI.8.2.027501

KEYWORDS: Image segmentation, Lung, Lawrencium, Tissues, Binary data, Biological research, Visualization, Blood vessels, Blood, Feature extraction

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Purpose: The mean linear intercept (MLI) score is a common metric for quantification of injury in lung histopathology images. The automated estimation of the MLI score is a challenging task because it requires accurate segmentation of different biological components of the lung tissue. Therefore, the most widely used approaches for MLI quantification are based on manual/semi-automated assessment of lung histopathology images, which can be expensive and time-consuming. We describe a fully automated pipeline for MLI estimation, which is capable of producing results comparable to human raters. Approach: We use a convolutional neural network based on U-Net architecture to segment the diagnostically relevant tissue segments in the whole slide images (WSI) of the mouse lung tissue. The proposed method extracts multiple field-of-view (FOV) images from the tissue segments and screen the FOV images, rejecting images based on presence of certain biological structures (i.e., blood vessels and bronchi). We used color slicing and region growing for segmentation of different biological structures in each FOV image. Results: The proposed method was tested on ten WSIs from mice and compared against the scores provided by three human raters. In segmenting the relevant tissue segments, our method obtained a mean accuracy, Dice coefficient, and Hausdorff distance of 98.34%, 98.22%, and 109.68 μm, respectively. Our proposed method yields a mean precision, recall, and F1-score of 93.37%, 83.47%, and 87.87%, respectively, in screening of FOV images. There was substantial agreement found between the proposed method and the manual scores (Fleiss Kappa score of 0.76). The mean difference between the calculated MLI score between the automated method and average rater’s score was 2.33 ± 4.13 (4.25 % ± 5.67 % ). Conclusion: The proposed pipeline for automated calculation of the MLI score demonstrates high consistency and accuracy with human raters and can be a potential replacement for manual/semi-automated approaches in the field.

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