Training Nuclei Detection Algorithms with Simple Annotations

MSAL-Net: improve accurate segmentation of nuclei in histopathology images by multiscale attention learning network

BACKGROUND

The digital pathology images obtain the essential information about the patient's disease, and the automated nuclei segmentation results can help doctors make better decisions about diagnosing the disease. With the speedy advancement of convolutional neural networks in image processing, deep learning has been shown to play a significant role in the various analysis of medical images, such as nuclei segmentation, mitosis detection and segmentation etc. Recently, several U-net based methods have been developed to solve the automated nuclei segmentation problems. However, these methods fail to deal with the weak features representation from the initial layers and introduce the noise into the decoder path. In this paper, we propose a multiscale attention learning network (MSAL-Net), where the dense dilated convolutions block captures more comprehensive nuclei context information, and a newly modified decoder part is introduced, which integrates with efficient channel attention and boundary refinement modules to effectively learn spatial information for better prediction and further refine the nuclei cell of boundaries.

RESULTS

Both qualitative and quantitative results are obtained on the publicly available MoNuseg dataset. Extensive experiment results verify that our proposed method significantly outperforms state-of-the-art methods as well as the vanilla Unet method in the segmentation task. Furthermore, we visually demonstrate the effect of our modified decoder part.

CONCLUSION

The MSAL-Net shows superiority with a novel decoder to segment the touching and blurred background nuclei cells obtained from histopathology images with better performance for accurate decoding.

Deep Learning Accurately Quantifies Plasma Cell Percentages on CD138-Stained Bone Marrow Samples

The diagnosis of plasma cell neoplasms requires accurate, and ideally precise, percentages. This plasma cell percentage is often determined by visual estimation of CD138-stained bone marrow biopsies and clot sections. While not necessarily inaccurate, estimates are by definition imprecise. For this study, we hypothesized that deep learning can be used to improve precision. We trained a semantic segmentation-based convolutional neural network (CNN) using annotations of CD138+ and CD138- cells provided by one pathologist on small image patches of bone marrow and validated the CNN on an independent test set of image patches using annotations from two pathologists and a non-deep learning commercial software. On validation, we found that the intraclass correlation coefficients for plasma cell percentages between the CNN and pathologist #1, a non-deep learning commercial software and pathologist #1, and pathologists #1 and #2 were 0.975, 0.892, and 0.994, respectively. The overall results show that CNN labels were almost as accurate as pathologist labels at a cell-by-cell level. Once satisfied with performance, we scaled-up the CNN to evaluate whole slide images (WSIs), and deployed the system as a workflow friendly web application to measure plasma cell percentages using snapshots taken from microscope cameras.

The human-in-the-loop: an evaluation of pathologists' interaction with artificial intelligence in clinical practice

AIMS

One of the major drivers of the adoption of digital pathology in clinical practice is the possibility of introducing digital image analysis (DIA) to assist with diagnostic tasks. This offers potential increases in accuracy, reproducibility, and efficiency. Whereas stand-alone DIA has great potential benefit for research, little is known about the effect of DIA assistance in clinical use. The aim of this study was to investigate the clinical use characteristics of a DIA application for Ki67 proliferation assessment. Specifically, the human-in-the-loop interplay between DIA and pathologists was studied.

METHODS AND RESULTS

We retrospectively investigated breast cancer Ki67 areas assessed with human-in-the-loop DIA and compared them with visual and automatic approaches. The results, expressed as standard deviation of the error in the Ki67 index, showed that visual estimation ('eyeballing') (14.9 percentage points) performed significantly worse (P < 0.05) than DIA alone (7.2 percentage points) and DIA with human-in-the-loop corrections (6.9 percentage points). At the overall level, no improvement resulting from the addition of human-in-the-loop corrections to the automatic DIA results could be seen. For individual cases, however, human-in-the-loop corrections could address major DIA errors in terms of poor thresholding of faint staining and incorrect tumour-stroma separation.

CONCLUSION

The findings indicate that the primary value of human-in-the-loop corrections is to address major weaknesses of a DIA application, rather than fine-tuning the DIA quantifications.

Deep learning nuclei detection: A simple approach can deliver state-of-the-art results

BACKGROUND

Deep convolutional neural networks have become a widespread tool for the detection of nuclei in histopathology images. Many implementations share a basic approach that includes generation of an intermediate map indicating the presence of a nucleus center, which we refer to as PMap. Nevertheless, these implementations often still differ in several parameters, resulting in different detection qualities.

METHODS

We identified several essential parameters and configured the basic PMap approach using combinations of them. We thoroughly evaluated and compared various configurations on multiple datasets with respect to detection quality, efficiency and training effort.

RESULTS

Post-processing of the PMap was found to have the largest impact on detection quality. Also, two different network architectures were identified that improve either detection quality or runtime performance. The best-performing configuration yields f1-measures of 0.816 on H&E stained images of colorectal adenocarcinomas and 0.819 on Ki-67 stained images of breast tumor tissue. On average, it was fully trained in less than 15,000 iterations and processed 4.15 megapixels per second at prediction time.

CONCLUSIONS

The basic PMap approach is greatly affected by certain parameters. Our evaluation provides guidance on their impact and best settings. When configured properly, this simple and efficient approach can yield equal detection quality as more complex and time-consuming state-of-the-art approaches.