Improving High Resolution Histology Image Classification with Deep Spatial Fusion Network

Histology imaging is an essential diagnosis method to finalize the grade and stage of cancer of different tissues, especially for breast cancer diagnosis. Specialists often disagree on the final diagnosis on biopsy tissue due to the complex morphological variety. Although convolutional neural networks (CNN) have advantages in extracting discriminative features in image classification, directly training a CNN on high resolution histology images is computationally infeasible currently. Besides, inconsistent discriminative features often distribute over the whole histology image, which incurs challenges in patch-based CNN classification method. In this paper, we propose a novel architecture for automatic classification of high resolution histology images. First, an adapted residual network is employed to explore hierarchical features without attenuation. Second, we develop a robust deep fusion network to utilize the spatial relationship between patches and learn to correct the prediction bias generated from inconsistent discriminative feature distribution. The proposed method is evaluated using 10-fold cross-validation on 400 high resolution breast histology images with balanced labels and reports 95% accuracy on 4-class classification and 98.5% accuracy, 99.6% AUC on 2-class classification (carcinoma and non-carcinoma), which substantially outperforms previous methods and close to pathologist performance.Comment: 8 pages, MICCAI workshop preceeding

Developing a practical guide for teaching histology: an evaluation of the didactic components

The Didactic Guide is a valuable tool complementing and making learning more dynamic. It is done using creative didactic strategies that simulate the presence of the tutor and generate a dialogue in order to offer students different possibilities to improve their understanding and self-discovery learning.This way the student is the protagonist of his own learning process. In this context, is highly important to consider the self discovery learning as a goal facilitating significant learning. The use of practical guides enables students to manage their own time, goals, techniques, contents and evaluation. In medical histology teaching several models of didactic guides could be use, and they normally include numerous activities, text, questionnaires, pictures, and drawings that may enhance the effectiveness of this tool in the learning process. In this work we have evaluated the usefulnes of different sections of a histology didactic guide in order to determine the key sections that enhance the learning process in human histology. For this purpose, a practical histology guide was designed with different sections: message text, theoretical text, objectives, drawings, pictures, clinical cases, games, blank spaces for self notes and drawing and final self evaluation questions. First, a simple questionnaire was applied in 90 students enrolled in histology practical seccions to analyze the student´s perceptions and preferences related to the histology guide. Finally, for all questionnaires average results and standard deviations were calculated for each option and all participants, as well for each gender, separately. Comparisons were done for drawings vs drawing blank spaces, teoric content vs notes blank space, drawings vs pictures and for each gender separately using Mann-Whitney non-parametrical test. Our findings revealed that visual strategies such as images and pictures were considered to be more useful for learning histology in the practical session. Similary, the students rated the self evaluation questions and blank spaces for self notes and drawing to be more attractive to the students. However, texts with theoretical information, messages, objectives, and clinical cases revealed to be less useful for the students in the learning process of medical histology. Moreover, statistically significant differences between theoretical content vs notes blank space was observed. All these results point out the importance of including pictures and drawings in the practical guide accompained of blank spaces that allow the development of creativity and autonomy that lead the students into a self discovery learning. Interestingly the students do not appreciate the presence of theoretical background in the practical guide as relevant information for their academic formation in the practical session

Part-to-whole Registration of Histology and MRI using Shape Elements

Image registration between histology and magnetic resonance imaging (MRI) is a challenging task due to differences in structural content and contrast. Too thick and wide specimens cannot be processed all at once and must be cut into smaller pieces. This dramatically increases the complexity of the problem, since each piece should be individually and manually pre-aligned. To the best of our knowledge, no automatic method can reliably locate such piece of tissue within its respective whole in the MRI slice, and align it without any prior information. We propose here a novel automatic approach to the joint problem of multimodal registration between histology and MRI, when only a fraction of tissue is available from histology. The approach relies on the representation of images using their level lines so as to reach contrast invariance. Shape elements obtained via the extraction of bitangents are encoded in a projective-invariant manner, which permits the identification of common pieces of curves between two images. We evaluated the approach on human brain histology and compared resulting alignments against manually annotated ground truths. Considering the complexity of the brain folding patterns, preliminary results are promising and suggest the use of characteristic and meaningful shape elements for improved robustness and efficiency.Comment: Paper accepted at ICCV Workshop (Bio-Image Computing

Histology Services

Creative Bioarray provides our global clients the most comprehensive histology services. Drawing on many years of experience and in-depth knowledge, Creative Bioarray offers tissue processing, embedding, sectioning, and staining. Besides a histological examination of all major organs/tissues is provided, including immunohistochemistry (IHC), immunofluorescence (IF), in situ hybridization (ISH), fluorescent in situ hybridization (FISH), and transmission electron mircoscopy. We also perform custom customized packages to satisfy your special requirements. Our experienced scientists work closely with you on every step and provide the most convenient service for you and your team

Quantitative Ultrasound and B-mode Image Texture Features Correlate with Collagen and Myelin Content in Human Ulnar Nerve Fascicles

We investigate the usefulness of quantitative ultrasound (QUS) and B-mode texture features for characterization of ulnar nerve fascicles. Ultrasound data were acquired from cadaveric specimens using a nominal 30 MHz probe. Next, the nerves were extracted to prepare histology sections. 85 fascicles were matched between the B-mode images and the histology sections. For each fascicle image, we selected an intra-fascicular region of interest. We used histology sections to determine features related to the concentration of collagen and myelin, and ultrasound data to calculate backscatter coefficient (-24.89 dB $\pm$ 8.31), attenuation coefficient (0.92 db/cm-MHz $\pm$ 0.04), Nakagami parameter (1.01 $\pm$ 0.18) and entropy (6.92 $\pm$ 0.83), as well as B-mode texture features obtained via the gray level co-occurrence matrix algorithm. Significant Spearman's rank correlations between the combined collagen and myelin concentrations were obtained for the backscatter coefficient (R=-0.68), entropy (R=-0.51), and for several texture features. Our study demonstrates that QUS may potentially provide information on structural components of nerve fascicles

Improving Whole Slide Segmentation Through Visual Context - A Systematic Study

While challenging, the dense segmentation of histology images is a necessary first step to assess changes in tissue architecture and cellular morphology. Although specific convolutional neural network architectures have been applied with great success to the problem, few effectively incorporate visual context information from multiple scales. With this paper, we present a systematic comparison of different architectures to assess how including multi-scale information affects segmentation performance. A publicly available breast cancer and a locally collected prostate cancer datasets are being utilised for this study. The results support our hypothesis that visual context and scale play a crucial role in histology image classification problems