Semantic segmentation to identify bladder layers from H&E Images

A lightweight bladder tumor segmentation method based on attention mechanism

In the endoscopic images of bladder, accurate segmentation of different grade bladder tumor from blurred boundary regions and highly variable shapes is of great significance for doctors' diagnosis and patients' later treatment. We propose a nested attentional feature fusion segmentation network (NAFF-Net) based on the encoder-decoder structure formed by the combination of weighted pyramid pooling module (WPPM) and nested attentional feature fusion (NAFF). Among them, WPPM applies the cascade of atrous convolution to enhance the overall perceptual field while introducing adaptive weights to optimize multi-scale feature extraction, NAFF integrates deep semantic information into shallow feature maps, effectively focusing on edge and detail information in bladder tumor images. Additionally, a weighted mixed loss function is constructed to alleviate the impact of imbalance between positive and negative sample distribution on segmentation accuracy. Experiments illustrate the proposed NAFF-Net achieves better segmentation results compared to other mainstream models, with a MIoU of 84.05%, MPrecision of 91.52%, MRecall of 90.81%, and F1-score of 91.16%, and also achieves good results on the public datasets Kvasir-SEG and CVC-ClinicDB. Compared to other models, NAFF-Net has a smaller number of parameters, which is a significant advantage in model deployment.

Whole Slide Imaging Technology and Its Applications: Current and Emerging Perspectives

Background. Whole slide imaging (WSI) represents a paradigm shift in pathology, serving as a necessary first step for a wide array of digital tools to enter the field. It utilizes virtual microscopy wherein glass slides are converted into digital slides and are viewed by pathologists by automated image analysis. Its impact on pathology workflow, reproducibility, dissemination of educational material, expansion of service to underprivileged areas, and institutional collaboration exemplifies a significant innovative movement. The recent US Food and Drug Administration approval to WSI for its use in primary surgical pathology diagnosis has opened opportunities for wider application of this technology in routine practice. Main Text. The ongoing technological advances in digital scanners, image visualization methods, and the integration of artificial intelligence-derived algorithms with these systems provide avenues to exploit its applications. Its benefits are innumerable such as ease of access through the internet, avoidance of physical storage space, and no risk of deterioration of staining quality or breakage of slides to name a few. Although the benefits of WSI to pathology practices are many, the complexities of implementation remain an obstacle to widespread adoption. Some barriers including the high cost, technical glitches, and most importantly professional hesitation to adopt a new technology have hindered its use in routine pathology. Conclusions. In this review, we summarize the technical aspects of WSI, its applications in diagnostic pathology, training, and research along with future perspectives. It also highlights improved understanding of the current challenges to implementation, as well as the benefits and successes of the technology. WSI provides a golden opportunity for pathologists to guide its evolution, standardization, and implementation to better acquaint them with the key aspects of this technology and its judicial use. Also, implementation of routine digital pathology is an extra step requiring resources which (currently) does not usually result increased efficiency or payment.

A New Method of Artificial-Intelligence-Based Automatic Identification of Lymphovascular Invasion in Urothelial Carcinomas

The presence of lymphovascular invasion (LVI) in urothelial carcinoma (UC) is a poor prognostic finding. This is difficult to identify on routine hematoxylin-eosin (H&E)-stained slides, but considering the costs and time required for examination, immunohistochemical stains for the endothelium are not the recommended diagnostic protocol. We developed an AI-based automated method for LVI identification on H&E-stained slides. We selected two separate groups of UC patients with transurethral resection specimens. Group A had 105 patients (100 with UC; 5 with cystitis); group B had 55 patients (all with high-grade UC; D2-40 and CD34 immunohistochemical stains performed on each block). All the group A slides and 52 H&E cases from group B showing LVI using immunohistochemistry were scanned using an Aperio GT450 automatic scanner. We performed a pixel-per-pixel semantic segmentation of selected areas, and we trained InternImage to identify several classes. The DiceCoefficient and Intersection-over-Union scores for LVI detection using our method were 0.77 and 0.52, respectively. The pathologists' H&E-based evaluation in group B revealed 89.65% specificity, 42.30% sensitivity, 67.27% accuracy, and an F1 score of 0.55, which is much lower than the algorithm's DCC of 0.77. Our model outlines LVI on H&E-stained-slides more effectively than human examiners; thus, it proves a valuable tool for pathologists.

CNN-Res: deep learning framework for segmentation of acute ischemic stroke lesions on multimodal MRI images

BACKGROUND

Accurate segmentation of stroke lesions on MRI images is very important for neurologists in the planning of post-stroke care. Segmentation helps clinicians to better diagnose and evaluation of any treatment risks. However, manual segmentation of brain lesions relies on the experience of neurologists and is also a very tedious and time-consuming process. So, in this study, we proposed a novel deep convolutional neural network (CNN-Res) that automatically performs the segmentation of ischemic stroke lesions from multimodal MRIs.

METHODS

CNN-Res used a U-shaped structure, so the network has encryption and decryption paths. The residual units are embedded in the encoder path. In this model, to reduce gradient descent, the residual units were used, and to extract more complex information in images, multimodal MRI data were applied. In the link between the encryption and decryption subnets, the bottleneck strategy was used, which reduced the number of parameters and training time compared to similar research.

RESULTS

CNN-Res was evaluated on two distinct datasets. First, it was examined on a dataset collected from the Neuroscience Center of Tabriz University of Medical Sciences, where the average Dice coefficient was equal to 85.43%. Then, to compare the efficiency and performance of the model with other similar works, CNN-Res was evaluated on the popular SPES 2015 competition dataset where the average Dice coefficient was 79.23%.

CONCLUSION

This study presented a new and accurate method for the segmentation of MRI medical images using a deep convolutional neural network called CNN-Res, which directly predicts segment maps from raw input pixels.

Artificial Intelligence in Digital Pathology for Bladder Cancer: Hype or Hope? A Systematic Review

Bladder cancer (BC) diagnosis and prediction of prognosis are hindered by subjective pathological evaluation, which may cause misdiagnosis and under-/over-treatment. Computational pathology (CPATH) can identify clinical outcome predictors, offering an objective approach to improve prognosis. However, a systematic review of CPATH in BC literature is lacking. Therefore, we present a comprehensive overview of studies that used CPATH in BC, analyzing 33 out of 2285 identified studies. Most studies analyzed regions of interest to distinguish normal versus tumor tissue and identify tumor grade/stage and tissue types (e.g., urothelium, stroma, and muscle). The cell's nuclear area, shape irregularity, and roundness were the most promising markers to predict recurrence and survival based on selected regions of interest, with >80% accuracy. CPATH identified molecular subtypes by detecting features, e.g., papillary structures, hyperchromatic, and pleomorphic nuclei. Combining clinicopathological and image-derived features improved recurrence and survival prediction. However, due to the lack of outcome interpretability and independent test datasets, robustness and clinical applicability could not be ensured. The current literature demonstrates that CPATH holds the potential to improve BC diagnosis and prediction of prognosis. However, more robust, interpretable, accurate models and larger datasets-representative of clinical scenarios-are needed to address artificial intelligence's reliability, robustness, and black box challenge.

Artificial Intelligence in the Advanced Diagnosis of Bladder Cancer-Comprehensive Literature Review and Future Advancement

Artificial intelligence is highly regarded as the most promising future technology that will have a great impact on healthcare across all specialties. Its subsets, machine learning, deep learning, and artificial neural networks, are able to automatically learn from massive amounts of data and can improve the prediction algorithms to enhance their performance. This area is still under development, but the latest evidence shows great potential in the diagnosis, prognosis, and treatment of urological diseases, including bladder cancer, which are currently using old prediction tools and historical nomograms. This review focuses on highly significant and comprehensive literature evidence of artificial intelligence in the management of bladder cancer and investigates the near introduction in clinical practice.

Which data subset should be augmented for deep learning? a simulation study using urothelial cell carcinoma histopathology images

BACKGROUND

Applying deep learning to digital histopathology is hindered by the scarcity of manually annotated datasets. While data augmentation can ameliorate this obstacle, its methods are far from standardized. Our aim was to systematically explore the effects of skipping data augmentation; applying data augmentation to different subsets of the whole dataset (training set, validation set, test set, two of them, or all of them); and applying data augmentation at different time points (before, during, or after dividing the dataset into three subsets). Different combinations of the above possibilities resulted in 11 ways to apply augmentation. The literature contains no such comprehensive systematic comparison of these augmentation ways.

RESULTS

Non-overlapping photographs of all tissues on 90 hematoxylin-and-eosin-stained urinary bladder slides were obtained. Then, they were manually classified as either inflammation (5948 images), urothelial cell carcinoma (5811 images), or invalid (3132 images; excluded). If done, augmentation was eight-fold by flipping and rotation. Four convolutional neural networks (Inception-v3, ResNet-101, GoogLeNet, and SqueezeNet), pre-trained on the ImageNet dataset, were fine-tuned to binary classify images of our dataset. This task was the benchmark for our experiments. Model testing performance was evaluated using accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve. Model validation accuracy was also estimated. The best testing performance was achieved when augmentation was done to the remaining data after test-set separation, but before division into training and validation sets. This leaked information between the training and the validation sets, as evidenced by the optimistic validation accuracy. However, this leakage did not cause the validation set to malfunction. Augmentation before test-set separation led to optimistic results. Test-set augmentation yielded more accurate evaluation metrics with less uncertainty. Inception-v3 had the best overall testing performance.

CONCLUSIONS

In digital histopathology, augmentation should include both the test set (after its allocation), and the remaining combined training/validation set (before being split into separate training and validation sets). Future research should try to generalize our results.

An update on computational pathology tools for genitourinary pathology practice: A review paper from the Genitourinary Pathology Society (GUPS)

Machine learning has been leveraged for image analysis applications throughout a multitude of subspecialties. This position paper provides a perspective on the evolutionary trajectory of practical deep learning tools for genitourinary pathology through evaluating the most recent iterations of such algorithmic devices. Deep learning tools for genitourinary pathology demonstrate potential to enhance prognostic and predictive capacity for tumor assessment including grading, staging, and subtype identification, yet limitations in data availability, regulation, and standardization have stymied their implementation.

Attention2majority: Weak multiple instance learning for regenerative kidney grading on whole slide images

Deep learning consistently demonstrates high performance in classifying and segmenting medical images like CT, PET, and MRI. However, compared to these kinds of images, whole slide images (WSIs) of stained tissue sections are huge and thus much less efficient to process, especially for deep learning algorithms. To overcome these challenges, we present attention2majority, a weak multiple instance learning model to automatically and efficiently process WSIs for classification. Our method initially assigns exhaustively sampled label-free patches with the label of the respective WSIs and trains a convolutional neural network to perform patch-wise classification. Then, an intelligent sampling method is performed in which patches with high confidence are collected to form weak representations of WSIs. Lastly, we apply a multi-head attention-based multiple instance learning model to do slide-level classification based on high-confidence patches (intelligently sampled patches). Attention2majority was trained and tested on classifying the quality of 127 WSIs (of regenerated kidney sections) into three categories. On average, attention2majority resulted in 97.4%±2.4 AUC for the four-fold cross-validation. We demonstrate that the intelligent sampling module within attention2majority is superior to the current state-of-the-art random sampling method. Furthermore, we show that the replacement of random sampling with intelligent sampling in attention2majority results in its performance boost (from 94.9%±3.1 to 97.4%±2.4 average AUC for the four-fold cross-validation). We also tested a variation of attention2majority on the famous Camelyon16 dataset, which resulted in 89.1%±0.8 AUC¹. When compared to random sampling, the attention2majority demonstrated excellent slide-level interpretability. It also provided an efficient framework to arrive at a multi-class slide-level prediction.