Hyperspectral Imaging Using Flexible Endoscopy for Laryngeal Cancer Detection

Hyperspectral imaging (HSI) is increasingly gaining acceptance in the medical field. Up until now, HSI has been used in conjunction with rigid endoscopy to detect cancer in vivo. The logical next step is to pair HSI with flexible endoscopy, since it improves access to hard-to-reach areas. While the flexible endoscope’s fiber optic cables provide the advantage of flexibility, they also introduce an interfering honeycomb-like pattern onto images. Due to the substantial impact this pattern has on locating cancerous tissue, it must be removed before the HS data can be further processed. Thereby, the loss of information is to minimize avoiding the suppression of small-area variations of pixel values. We have developed a system that uses flexible endoscopy to record HS cubes of the larynx and designed a special filtering technique to remove the honeycomb-like pattern with minimal loss of information. We have confirmed its feasibility by comparing it to conventional filtering techniques using an objective metric and by applying unsupervised and supervised classifications to raw and pre-processed HS cubes. Compared to conventional techniques, our method successfully removes the honeycomb-like pattern and considerably improves classification performance, while preserving image details

Spectral-spatial Recurrent-Convolutional Networks for In-Vivo Hyperspectral Tumor Type Classification

Early detection of cancerous tissue is crucial for long-term patient survival. In the head and neck region, a typical diagnostic procedure is an endoscopic intervention where a medical expert manually assesses tissue using RGB camera images. While healthy and tumor regions are generally easier to distinguish, differentiating benign and malignant tumors is very challenging. This requires an invasive biopsy, followed by histological evaluation for diagnosis. Also, during tumor resection, tumor margins need to be verified by histological analysis. To avoid unnecessary tissue resection, a non-invasive, image-based diagnostic tool would be very valuable. Recently, hyperspectral imaging paired with deep learning has been proposed for this task, demonstrating promising results on ex-vivo specimens. In this work, we demonstrate the feasibility of in-vivo tumor type classification using hyperspectral imaging and deep learning. We analyze the value of using multiple hyperspectral bands compared to conventional RGB images and we study several machine learning models’ ability to make use of the additional spectral information. Based on our insights, we address spectral and spatial processing using recurrent-convolutional models for effective spectral aggregating and spatial feature learning. Our best model achieves an AUC of 76.3%, significantly outperforming previous conventional and deep learning methods

Diagnosis of myocardial infarction based on lectin-induced ethythrocyte agglutination: a feasibility study

Myocardial infarction (MI) is an acute life-threatening disease with a high incidence worldwide. Aim of this study was to test lectin-carbohydrate binding-induced red blood cell (RBC) agglutination as an innovative tool for fast, precise and cost effective diagnosis of MI. Five lectins (Ricinus communis agglutinin (RCA), Phaseolus vulgaris erythroagglutinin (PHA), Datura stramonium agglutinin (DSA), Artocarpus agglutinin (ArA), Triticum agglutinin (TA)) were tested for ability to differentiate between agglutination characteristics in patients with MI (n = 101) or angina pectoris without MI (AP) (n = 34) and healthy volunteers (HV) as control (n =68) . RBC agglutination was analyzed by light absorbance of a stirred RBC suspension in the green to red light spectrum in an agglutimeter (amtec, Leipzig, Germany) for 15 min after lectin addition. Mean cell count in aggregates was estimated from light absorbance by a mathematical model. Each lectin induced RBC agglutination. RCA led to the strongest RBC agglutination (~500 RBCs/aggregate), while the others induced substantially slower agglutination and lead to smaller aggregate sizes (5-150 RBCs/aggregate). For all analyzed lectins the lectin-induced RBC agglutination of MI or AP patients was generally higher than for HV. However, only PHA induced agglutination that clearly distinguished MI from HV. Variance analysis showed that aggregate size after 15 min. agglutination induced by PHA was significantly higher in the MI group (143 RBCs/ aggregate) than in the HV (29 RBC-s/aggregate, p = 0.000). We hypothesize that pathological changes during MI induce modification of the carbohydrate composition on the RBC membrane and thus modify RBC agglutination. Occurrence of carbohydrate-lectin binding sites on RBC membranes provides evidence about MI. Due to significant difference in the rate of agglutination between MI > HV the differentiation between these groups is possible based on PHA-induced RBC-agglutination. This novel assay could serve as a rapid, cost effective valuable new tool for diagnosis of MI