BUS-Set:A benchmark for quantitative evaluation of breast ultrasound segmentation networks with public datasets

Purpose: BUS-Set is a reproducible benchmark for breast ultrasound (BUS) lesion segmentation, comprising of publicly available images with the aim of improving future comparisons between machine learning models within the field of BUS. Method: Four publicly available datasets were compiled creating an overall set of 1154 BUS images, from five different scanner types. Full dataset details have been provided, which include clinical labels and detailed annotations. Furthermore, nine state-of-the-art deep learning architectures were selected to form the initial benchmark segmentation result, tested using five-fold cross-validation and MANOVA/ANOVA with Tukey statistical significance test with a threshold of 0.01. Additional evaluation of these architectures was conducted, exploring possible training bias, and lesion size and type effects. Results: Of the nine state-of-the-art benchmarked architectures, Mask R-CNN obtained the highest overall results, with the following mean metric scores: Dice score of 0.851, intersection over union of 0.786 and pixel accuracy of 0.975. MANOVA/ANOVA and Tukey test results showed Mask R-CNN to be statistically significant better compared to all other benchmarked models with a p-value >0.01. Moreover, Mask R-CNN achieved the highest mean Dice score of 0.839 on an additional 16 image dataset, that contained multiple lesions per image. Further analysis on regions of interest was conducted, assessing Hamming distance, depth-to-width ratio (DWR), circularity, and elongation, which showed that the Mask R-CNN's segmentations maintained the most morphological features with correlation coefficients of 0.888, 0.532, 0.876 for DWR, circularity, and elongation, respectively. Based on the correlation coefficients, statistical test indicated that Mask R-CNN was only significantly different to Sk-U-Net. Conclusions: BUS-Set is a fully reproducible benchmark for BUS lesion segmentation obtained through the use of public datasets and GitHub. Of the state-of-the-art convolution neural network (CNN)-based architectures, Mask R-CNN achieved the highest performance overall, further analysis indicated that a training bias may have occurred due to the lesion size variation in the dataset. All dataset and architecture details are available at GitHub: https://github.com/corcor27/BUS-Set, which allows for a fully reproducible benchmark

An annotated cDNA library of juvenile Euprymna scolopes with and without colonization by the symbiont Vibrio fischeri

BACKGROUND: Biologists are becoming increasingly aware that the interaction of animals, including humans, with their coevolved bacterial partners is essential for health. This growing awareness has been a driving force for the development of models for the study of beneficial animal-bacterial interactions. In the squid-vibrio model, symbiotic Vibrio fischeri induce dramatic developmental changes in the light organ of host Euprymna scolopes over the first hours to days of their partnership. We report here the creation of a juvenile light-organ specific EST database. RESULTS: We generated eleven cDNA libraries from the light organ of E. scolopes at developmentally significant time points with and without colonization by V. fischeri. Single pass 3' sequencing efforts generated 42,564 expressed sequence tags (ESTs) of which 35,421 passed our quality criteria and were then clustered via the UIcluster program into 13,962 nonredundant sequences. The cDNA clones representing these nonredundant sequences were sequenced from the 5' end of the vector and 58% of these resulting sequences overlapped significantly with the associated 3' sequence to generate 8,067 contigs with an average sequence length of 1,065 bp. All sequences were annotated with BLASTX (E-value < -03) and Gene Ontology (GO). CONCLUSION: Both the number of ESTs generated from each library and GO categorizations are reflective of the activity state of the light organ during these early stages of symbiosis. Future analyses of the sequences identified in these libraries promise to provide valuable information not only about pathways involved in colonization and early development of the squid light organ, but also about pathways conserved in response to bacterial colonization across the animal kingdom