On the representation of cells in bone marrow pathology by a scalar field: propagation through serial sections, co-localization and spatial interaction analysis

DetexNet: Accurately Diagnosing Frequent and Challenging Pediatric Malignant Tumors

The most frequent extracranial solid tumors of childhood, named peripheral neuroblastic tumors (pNTs), are very challenging to diagnose due to their diversified categories and varying forms. Auxiliary diagnosis methods of such pediatric malignant cancers are highly needed to provide pathologists assistance and reduce the risk of misdiagnosis before treatments. In this paper, inspired by the particularity of microscopic pathology images, we integrate neural networks with the texture energy measure (TEM) and propose a novel network architecture named DetexNet (deep texture network). This method enforces the low-level representation pattern clearer via embedding the expert knowledge as prior, so that the network can seize the key information of a relatively small pathological dataset more smoothly. By applying and finetuning TEM filters in the bottom layer of a network, we greatly improve the performance of the baseline. We further pre-train the model on unlabeled data with an auto-encoder architecture and implement a color space conversion on input images. Two kinds of experiments under different assumptions in the condition of limited training data are performed, and in both of them, the proposed method achieves the best performance compared with other state-of-the-art models and doctor diagnosis.

Automatic evaluation of tumor budding in immunohistochemically stained colorectal carcinomas and correlation to clinical outcome

Background

Tumor budding, meaning a detachment of tumor cells at the invasion front of colorectal carcinoma (CRC) into single cells or clusters (<=5 tumor cells), has been shown to correlate to an inferior clinical outcome by several independent studies. Therefore, it has been discussed as a complementary prognostic factor to the TNM staging system, and it is already included in national guidelines as an additional prognostic parameter. However, its application by manual evaluation in routine pathology is hampered due to the use of several slightly different assessment systems, a time-consuming manual counting process and a high inter-observer variability. Hence, we established and validated an automatic image processing approach to reliably quantify tumor budding in immunohistochemically (IHC) stained sections of CRC samples.

Methods

This approach combines classical segmentation methods (like morphological operations) and machine learning techniques (k-means and hierarchical clustering, convolutional neural networks) to reliably detect tumor buds in colorectal carcinoma samples immunohistochemically stained for pan-cytokeratin. As a possible application, we tested it on whole-slide images as well as on tissue microarrays (TMA) from a clinically well-annotated CRC cohort.

Results

Our automatic tumor budding evaluation tool detected the absolute number of tumor buds per image with a very good correlation to the manually segmented ground truth (R2 value of 0.86).

Furthermore the automatic evaluation of whole-slide images from 20 CRC-patients, we found that neither the detected number of tumor buds at the invasion front nor the number in hotspots was associated with the nodal status. However, the number of spatial clusters of tumor buds (budding hotspots) significantly correlated to the nodal status (p-value = 0.003 for N0 vs. N1/N2). TMAs were not feasible for tumor budding evaluation, as the spatial relationship of tumor buds (especially hotspots) was not preserved.

Conclusions

Automatic image processing is a feasible and valid assessment tool for tumor budding in CRC on whole-slide images. Interestingly, only the spatial clustering of the tumor buds in hotspots (and especially the number of hotspots) and not the absolute number of tumor buds showed a clinically relevant correlation with patient outcome in our data.

Electronic supplementary material

The online version of this article (10.1186/s13000-018-0739-3) contains supplementary material, which is available to authorized users.

Three-dimensional digital reconstruction of skin epidermis and dermis

This study describes how three-dimensional (3D) human skin tissue is reconstructed, and provides digital anatomical data for the physiological structure of human skin tissue based on large-scale thin serial sections. Human skin samples embedded in paraffin were cut serially into thin sections and then stained with hematoxylin-eosin. Images of serial sections obtained from lighting microscopy were scanned and aligned by the scale-invariant feature transform algorithm. 3D reconstruction of the skin tissue was generated using Mimics software. Fibre content, porosity, average pore diameter and specific surface area of dermis were analysed using the ImageJ analysis system. The root mean square error and mutual information based on the scale-invariant feature transform algorithm registration were significantly greater than those based on the manual registration. Fibre distribution gradually decreased from top to bottom; while porosity showed an opposite trend with irregular average pore diameter distribution. A specific surface area of the dermis showed a 'V' shape trend. Our data suggested that 3D reconstruction of human skin tissue based on large-scale serial sections could be a valuable tool for providing a highly accurate histological structure for analysis of skin tissue. Moreover, this technology could be utilized to produce tissue-engineered skin via a 3D bioprinter in the future.