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  • 1. Introduction Materials and Methods Results Discussion Automatic Classification of Optical Biopsy Images Ruben Tous1 & Prof. Dr. Ferrer Roca 2 1Universitat Politècnica de Catalunya. Departament d’Arquitectura de Computadors 2 Unesco Chair of Telemedicine. Canary Islands. Spain July 2013 Tous Automatic Classification of Optical Biopsy Images
  • 2. Introduction Materials and Methods Results Discussion Índex 1 Introduction 2 Materials and Methods 3 Results 4 Discussion Tous Automatic Classification of Optical Biopsy Images
  • 3. Introduction Materials and Methods Results Discussion Índex 1 Introduction 2 Materials and Methods 3 Results 4 Discussion Tous Automatic Classification of Optical Biopsy Images
  • 4. Introduction Materials and Methods Results Discussion Introduction Problem statement Confocal laser endomicroscopy (CLE) has revolutionized gastrointestinal endoscopy by providing microscopic visualization on a cellular basis in vivo. However, most gastroenterologists are not trained to interpret mucosal pathology, and histopathologists are usually not available in the endoscopy suite. Tous Automatic Classification of Optical Biopsy Images
  • 5. Introduction Materials and Methods Results Discussion Introduction General project goal To build a set of computer-aided diagnosis tools that assist endoscopists in the interpretation of optical biopsies obtained through CLE. Real-time functionalities for supporting diagnosis in vivo. Tools for cataloguing and searching CLE databases. Tous Automatic Classification of Optical Biopsy Images
  • 6. Introduction Materials and Methods Results Discussion Introduction Project subgoals Real-time functionalities for supporting diagnosis in vivo. Automated diagnosis. (normal vs. abnormal mucosa, differentiation between different pathologies). Overprint visual markers highlighting the geometry of the crypts. Tools for cataloguing and searching CLE databases. Automated cataloguing of CLE databases. Search engine with query-by-example functionality. Tous Automatic Classification of Optical Biopsy Images
  • 7. Introduction Materials and Methods Results Discussion Introduction. Colon microarchitecture Tous Automatic Classification of Optical Biopsy Images
  • 8. Introduction Materials and Methods Results Discussion Introduction. Colouring agents Left: acrifliavine hydrochloride. Right: fluorescein (used for our sample). Red arrows: goblet cells. Tous Automatic Classification of Optical Biopsy Images
  • 9. Introduction Materials and Methods Results Discussion Índex 1 Introduction 2 Materials and Methods 3 Results 4 Discussion Tous Automatic Classification of Optical Biopsy Images
  • 10. Introduction Materials and Methods Results Discussion Materials and Methods. General workflow Tous Automatic Classification of Optical Biopsy Images
  • 11. Introduction Materials and Methods Results Discussion Materials and Methods. General workflow In a first step, the system’s database was standardized using image sets based on the Mainz confocal classification for colonic optical biopsies. This step was used to provide an optical biopsy search engine based on extracting simplified features of crypt and mucosal patterns. The second step aims at providing an interrogation platform to provide on-site assistance during CLE, mainly by automated detection of crypt geometry. Tous Automatic Classification of Optical Biopsy Images
  • 12. Introduction Materials and Methods Results Discussion Materials and Methods. Image set description 137 black and white instances 10 are labelled as "healthy" 12 as "hyperplastic" 115 as neoplastic" Instances have 1024x1024 pixels, and are 8-bit grayscale, JPEG compressed and 548KB in average. Many of the instances were taken from same patients, and some of them only capture slight variations of a same tissue. Tous Automatic Classification of Optical Biopsy Images
  • 13. Introduction Materials and Methods Results Discussion Materials and Methods. Class description Each image instance is tagged according to medical judgement, the three main categories being: Healthy Hyperplastic Neoplastic Neoplastic comprises Adenomous and Cancerous, each of them in several degrees of seriousness and malignity, namely normalör low gradefor adenomas, and normaländ Type 1for carcinomas. Sample instances can be categorised via visual inspection, at least into the three main categories. Tous Automatic Classification of Optical Biopsy Images
  • 14. Introduction Materials and Methods Results Discussion Materials and Methods. Healthy tissue Healthy tissue is characterised by regular crypts in a honeycomb-like structure that present small size and shape variance. In general, healthy crypts are recognisable as darker, circle-shaped areas in a generally regular spatial distribution. Due to variations in the depth or diferent degrees of fluorescent agent penetration, the cells’ cytoplasm may be especially prominent (bottom). This circumstance has proven, at later stages, to be non-trivial to address. Tous Automatic Classification of Optical Biopsy Images
  • 15. Introduction Materials and Methods Results Discussion Materials and Methods. Hyperplastic tissue Hyperplasia means cells have proliferated more than usual in a tissue. In hyperplastic colon cases we can appreciate abnormal crypt bloating, although their integrity is still preserved to some extent. Tous Automatic Classification of Optical Biopsy Images
  • 16. Introduction Materials and Methods Results Discussion Materials and Methods. Hyperplastic tissue Some milder cases present a regular crypt structure just like in healthy tissue, except in one or two crypts that have abnormal growth. In contrast with these, some others are more dificult to recognise. Tous Automatic Classification of Optical Biopsy Images
  • 17. Introduction Materials and Methods Results Discussion Materials and Methods. Neoplastic tissue Neoplasia is the massive and uncoordinated proliferation of cells; this is also commonly known as tumour. Tous Automatic Classification of Optical Biopsy Images
  • 18. Introduction Materials and Methods Results Discussion Materials and Methods. Neoplastic tissue A typical feature in them is having strange nuclear-to-cytoplasmic ratios. However, nuclei are not stained by fluorescein. Fortunately, neoangiogenesis can be observed with fluorescein, and it is a phenomenon whose detection points strongly towards pathogenesis. These new blood vessels are caracterised not only by tortuosity and high irregularity, but also by profuse liquid leakiness, that causes tissue staining easy to see. Tous Automatic Classification of Optical Biopsy Images
  • 19. Introduction Materials and Methods Results Discussion Materials and Methods. Algorithm outline Automatic feature extraction in CLE images. Inferring semantic metadata from low-level features. Image Analysis + Pattern Recognition. The image analysis algorithm allows identifying the different crypts and also featuring their contours. The extracted low-level visual features are then combined to obtain a feature vector. This vector is analyzed to translate the low-level details into high-level semantic information about the images, notably a suggested diagnosis. Tous Automatic Classification of Optical Biopsy Images
  • 20. Introduction Materials and Methods Results Discussion Materials and Methods. Algorithm flowchart Tous Automatic Classification of Optical Biopsy Images
  • 21. Introduction Materials and Methods Results Discussion Índex 1 Introduction 2 Materials and Methods 3 Results 4 Discussion Tous Automatic Classification of Optical Biopsy Images
  • 22. Introduction Materials and Methods Results Discussion Results. Automatic crypt geometry characterization The image analysis algorithm allows identifying the different crypts and also featuring their contours with high accuracy. This information is computed to characterize the geometry of the crypts and to overprint visual markers aiming to facilitate diagnosis. Tous Automatic Classification of Optical Biopsy Images
  • 23. Introduction Materials and Methods Results Discussion Results. Automatic crypt geometry characterization Two watershedding flavours on a neoplastic image Tous Automatic Classification of Optical Biopsy Images
  • 24. Introduction Materials and Methods Results Discussion Results. Automated diagnosis Our classification approach Chaining two binary classification problems in a row. 1) healthy vs. unhealthy; if unhealthy 2) hyperplasia vs neoplasia. We opted for this because the crypt shape, boundary, etc on hyperplastic examples were not much different from those obtained from neoplastic ones. All classifers were evaluated using Leave One Out Cross-Validation (LOOCV). Tous Automatic Classification of Optical Biopsy Images
  • 25. Introduction Materials and Methods Results Discussion Results. Automated diagnosis (step 1) healthy vs. unhealthy Effectiveness (with 200x200 pixels downscaling and a custom classifier). A 100% hit rate is attained with the present algorithm. The present sample (137 images) is relatively small, and its classes are highly unbalanced in size. Besides, they come from an even smaller number of patients and capturing situations. Tous Automatic Classification of Optical Biopsy Images
  • 26. Introduction Materials and Methods Results Discussion Results. Automated diagnosis Confusion Matrix (custom classifer healthy vs. unhealthy) classified as unhealthy unhealthy healthy classified as healthy 127 0 0 10 Tous Automatic Classification of Optical Biopsy Images
  • 27. Introduction Materials and Methods Results Discussion Results. Automated diagnosis (step 2) hyperplasia vs neoplasia Classfication over the resulting unhealthy examples from step 1. We noted that cells were usually abundant and homogeneously distributed in hyperplastic images. We implemented a simple adhoc decision tree on the only 3 descriptors for the second decision by seeing how they behaved. It also reached 100% prediction rate within unhealthy images when dividing them in hyperplasia and neoplasia. Tous Automatic Classification of Optical Biopsy Images
  • 28. Introduction Materials and Methods Results Discussion Results. Automated diagnosis Efficiency We have studied the impact of the parameters choice on time costs, aiming to reduce them at the least possible accuracy expense. With the current performance, we believe real-time analysis of the newly acquired images is possible, thus aiding the endoscopist in his or her exploration task. In addition, this suggests a possible future line of progress: work towards a video-rate (24 frames per second) processing system, where crypt highlighting could be especially valuable. Tous Automatic Classification of Optical Biopsy Images
  • 29. Introduction Materials and Methods Results Discussion Results. Cataloguing and search Besides supporting the diagnosis of a single image during an ongoing session, the described method also allows annotating a complete database of CLE images with semantic information, thus providing a search engine with advanced functionalities such as semantic retrieval or query by image. Tous Automatic Classification of Optical Biopsy Images
  • 30. Introduction Materials and Methods Results Discussion Índex 1 Introduction 2 Materials and Methods 3 Results 4 Discussion Tous Automatic Classification of Optical Biopsy Images
  • 31. Introduction Materials and Methods Results Discussion Discussion On-site image analysis and semantic interpretation are able to provide a standardized diagnosis of CLE images in real time. This has the potential to facilitate, standardize and shorten endoscopists’ training for CLE. In addition, we provide a powerful tool to explore databases of images for retrospective analyses. This system’s geometry is not limited to CLE, but can be extended to multiple imaging modalities. Tous Automatic Classification of Optical Biopsy Images