Deep Convolutional Neural Networks for Histological Image Analysis in Gastric Carcinoma Whole Slide Images

Introduction/ Background In this paper, histopathological whole slide images of gastric carcinoma are analyzed using deep learning methods. A convolutional neural network architecture is proposed for two classification applications in H&E stained tissue images, namely, cancer classification based on immunohistochemistry (IHC) into classes Her2/neu+ tumor, Her2/neu- tumor and non-tumor, and necrosis detection based on existence of necrosis into classes necrotic and non-necrotic. The studies in [1] and [2] explored computer-aided classification using graphbased methods and necrosis detection by textural approach respectively, which are extended using deep convolutional neural networks. Performance is quantitatively compared with established handcrafted image features, namely Haralick GLCM, Gabor filter-banks, LBP histograms, Gray histograms, RGB histograms and HSV histograms followed by classification by random forests, another well-known machine learning algorithm. Aims Convolutional neural networks (CNN) have recently gained tremendous attention in general image analysis [3-5]. There has also been an emergence of deep learning in digital histopathology for diverse classification and detection problems [6-8]. The prime motivation behind this work is that no previous study has explored deep learning for the specified goals in gastric cancer WSI. Automated cancer classification can assist pathologists in computer-aided diagnosis in H&E stained WSI without the requirement of IHC staining, thereby reducing preparation and inspection times, and decreasing inter- and intra-observer variability. Necrosis detection can play an important role in prognosis, as larger necrotic areas indicate a smaller chance of survival and vice-versa. Moreover, most deep learning studies have used smaller image sizes mainly due to memory restrictions of GPU, however, we consider larger regions in order to preserve context i.e. neighborhood information and tissue architecture at higher magnification. Further, this method is independent of nuclei segmentation, hence its performance is not limited by segmentation performance as in [1] (evaluation details in [9]). Methods Firstly, standard data augmentation techniques are applied on the available gastric cancer WSI dataset and thousands of images of size 512x512 are generated. Different CNN architectures are empirically studied to observe the behavior of variation in model characteristics (network depth, layer properties, training parameters, etc.) by training them from scratch on a representative subset of whole data for cancer classification. One of these is the Imagenet model [4], however it doesn’t perform desirably on the representative dataset. The self-designed CNN architecture with best classification rates is selected. Later, the proposed CNN is also applied for necrosis detection. Performance is compared with state of the art methods using handcrafted features and random forests. For evaluation, randomized three-fold stratified shuffle split and leave-one-patient-out cross validations are used. Results Conclusion: A self-designed CNN architecture is proposed for image analysis (cancer classification based on IHC and necrosis detection) in H&E stained WSI of gastric cancer. Quantitative evaluation shows that deep learning methods mostly compare favorably to state of the art methods, especially for necrosis detection. In future the aim is to expand the current WSI dataset and to improve the CNN architecture for optimal performance

Computer Vision for Tissue Characterization and Outcome Prediction in Cancer

The aim of this dissertation was to investigate the use of computer vision for tissue characterization and patient outcome prediction in cancer. This work focused on analysis of digitized tissue specimens, which were stained only for basic morphology (i.e. hematoxylin and eosin). The applicability of texture analysis and convolutional neural networks was evaluated for detection of biologically and clinically relevant features. Moreover, novel approaches to guide ground-truth annotation and outcome-supervised learning for prediction of patient survival directly from the tumor tissue images without expert guidance was investigated. We first studied quantification of tumor viability through segmentation of necrotic and viable tissue compartments. We developed a regional texture analysis method, which was trained and tested on whole sections of mouse xenograft models of human lung cancer. Our experiments showed that the proposed segmentation was able to discriminate between viable and non-viable tissue regions with high accuracy when compared to human expert assessment. We next investigated the feasibility of pre-trained convolutional neural networks in analysis of breast cancer tissue, aiming to quantify tumor-infiltrating lymphocytes in the specimens. Interestingly, our results showed that pre-trained convolutional neural networks can be adapted for analysis of histological image data, outperforming texture analysis. The results also indicated that the computerized assessment was on par with pathologist assessments. Moreover, the study presented an image annotation technique guided by specific antibody staining for improved ground-truth labeling. Direct outcome prediction in breast cancer was then studied using a nationwide patient cohort. A computerized pipeline, which incorporated orderless feature aggregation and convolutional image descriptors for outcome-supervised classification, resulted in a risk grouping that was predictive of both disease-specific and overall survival. Surprisingly, further analysis suggested that the computerized risk prediction was also an independent prognostic factor that provided information complementary to the standard clinicopathological factors. This doctoral thesis demonstrated how computer-vision methods can be powerful tools in analysis of cancer tissue samples, highlighting strategies for supervised characterization of tissue entities and an approach for identification of novel prognostic morphological features.Kudosnäytteiden mikroskooppisten piirteiden visuaalinen tarkastelu on yksi tärkeimmistä määrityksistä syöpäpotilaiden diagnosoinnissa ja hoidon suunnittelussa. Edistyneet kuvantamisteknologiat ovat mahdollistaneet histologisten kasvainkudosnäytteiden digitalisoinnin tarkalla resoluutiolla. Näytteiden digitalisoinnin seurauksena niiden analysointiin voidaan soveltaa edistyneitä koneoppimiseen perustuvia konenäön menetelmiä. Tämä väitöskirja tutkii konenäön menetelmien soveltamista syöpäkudosnäytteiden laskennalliseen analyysiin. Työssä tutkitaan yksittäisten histologisten entiteettien, kuten nekroottisen kudoksen ja immuunisolujen automaattista kvantifiointia. Lisäksi työssä esitellään menetelmä potilaan selviytymisen ennustamiseen pelkkään kudosmorfologiaan perustuen