Histopathological examination of animal tissue by pathologists forms a crucial part of preclinical drug development. Machine learning techniques have been utilized to develop increasingly reliable and accurate analytical solutions in tissue imaging in recent years. Accurate assessment of the estrous cycle in rats and mice is important in evaluating pathogenesis and identifying potential test article related toxicity in toxicologic studies. In this paper, we present a Deep-Learning based framework for the classification of different stages of the Estrous cycle using whole slide images (WSI) of Hematoxylin & Eosin (H&E) stained sections from the Wistar rat vagina. We present an encoder-decoder convolution neural network, RAE-Net, based on three key criteria: Residual blocks for the decoder, Attention gate in the skip connection, and EfficientNetB4 for the encoder backbone. We show that the architecture achieves significant performance improvement over state-of-the-art segmentation architecture. The proposed estrous staging system could advance the pathology workflow in female preclinical reproductive toxicology studies.
Congenital heart disease is the leading cause of birth defect related deaths. The modified myocardial performance index of the right ventricle (R-MPI) is a sensitive and early clinical indicator of fetal cardiac health. Objective repeatable measurement of R-MPI is an important deciding factor for the clinical adaptation of the R-MPI. In this work, we describe a novel method for automatic computation of R-MPI from the Pulsed Wave Doppler (PWD) images. Our method involves a Fourier series based cardiac cycle detection followed by an adaptive windowed energy based valve click localization and weighted gradient based refinement. Using this method, we have been able to measure R-MPI reliably with a mean difference of 0.0075 ± 0.034 from 170 expert annotations on 68 fetal PWD images with an Intra-Class Correlation (ICC) of 0.9380. Furthermore, we have introduced novel methods for normalization and synchronization of PWD images acquired at two different time intervals for the assessment of iso-volume time intervals and an accurate measurement of R-MPI.
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