Tubule-U-Net: a novel dataset and deep learning-based tubule segmentation framework in whole slide images of breast cancer.

The tubule index is a vital prognostic measure in breast cancer tumor grading and is visually evaluated by pathologists. In this paper, a computer-aided patch-based deep learning tubule segmentation framework, named Tubule-U-Net, is developed and proposed to segment tubules in Whole Slide Images (WSI) of breast cancer. Moreover, this paper presents a new tubule segmentation dataset consisting of 30820 polygonal annotated tubules in 8225 patches. The Tubule-U-Net framework first uses a patch enhancement technique such as reflection or mirror padding and then employs an asymmetric encoder-decoder semantic segmentation model. The encoder is developed in the model by various deep learning architectures such as EfficientNetB3, ResNet34, and DenseNet161, whereas the decoder is similar to U-Net. Thus, three different models are obtained, which are EfficientNetB3-U-Net, ResNet34-U-Net, and DenseNet161-U-Net. The proposed framework with three different models, U-Net, U-Net++, and Trans-U-Net segmentation methods are trained on the created dataset and tested on five different WSIs. The experimental results demonstrate that the proposed framework with the EfficientNetB3 model trained on patches obtained using the reflection padding and tested on patches with overlapping provides the best segmentation results on the test data and achieves 95.33%, 93.74%, and 90.02%, dice, recall, and specificity scores, respectively.

A deep-learning model for transforming the style of tissue images from cryosectioned to formalin-fixed and paraffin-embedded.

Histological artefacts in cryosectioned tissue can hinder rapid diagnostic assessments during surgery. Formalin-fixed and paraffin-embedded (FFPE) tissue provides higher quality slides, but the process for obtaining them is laborious (typically lasting 12-48 h) and hence unsuitable for intra-operative use. Here we report the development and performance of a deep-learning model that improves the quality of cryosectioned whole-slide images by transforming them into the style of whole-slide FFPE tissue within minutes. The model consists of a generative adversarial network incorporating an attention mechanism that rectifies cryosection artefacts and a self-regularization constraint between the cryosectioned and FFPE images for the preservation of clinically relevant features. Transformed FFPE-style images of gliomas and of non-small-cell lung cancers from a dataset independent from that used to train the model improved the rates of accurate tumour subtyping by pathologists.

Evaluation of the utility of early routine computed tomography angiography in subarachnoid hemorrhage patient outcomes.

OBJECTIVES: The role of an early CTA approach in neurologically stable patients with nontraumatic SAH has not been assessed. This study explored the use of CTA in clinically stable SAH patients to pre-emptively identify cerebral vasospasm, to evaluate whether this approach is associated with improved clinical outcomes. METHODS: We conducted a retrospective chart review of SAH patients presenting between July 2007 and December 2016 in a single academic center. Patients were divided into two groups: (1) Early CTA (stable patients who underwent a CTA between days 5-8 post-SAH), and (2) Standard Protocol. The co-primary outcomes were a composite of the mRS at discharge and last clinical follow-up (good = 0-2; poor = 3-6). A multivariable binary logistic regression was conducted to compare both groups against outcomes, controlling for potential confounders. RESULTS: A total of 415 patients were included, 103 (24.8%) with early CTA, and 312 (75.2%) undergoing the standard protocol; the mean age was 57 years and 248 (59.8%) patients were female. Patients in the early CTA group had a higher modified Fisher grade (3-4) (87.4% vs 63.1%; p < 0.02). The multivariable analysis showed that early CTA was independently associated with lower poor outcomes at discharge (OR = 0.21, 95% CI 0.07-0.61, p = 0.004). Plus, vasospasm detection was associated with an increased risk of poor outcomes (OR = 4.77, 95% CI 1.41 - 16.10, p = 0.01). Early CTA was not associated with outcomes at clinical follow-up. CONCLUSION: The early CTA surveillance approach was associated with better functional outcomes at discharge when compared to the current imaging standard practice.