Deep detection network for quantifying esophageal mucosa IPCLs vascular morphological distribution

Abstract

The invention belongs to the technical field of medical image processing, and particularly relates to a deep detection network for quantifying esophageal mucosa IPCLs vascular morphological distribution. The deep detection network comprises a feature extraction network, a feature pyramid, a region candidate network, an interest region pooling and clustering distribution priori self-embedded cancerlesion classification network and a system for visualization on a narrow-band imaging endoscope image. The feature extraction network extracts a feature map of the input image; the feature pyramid fuses the features of different scales; the region candidate network proposes a possible lesion region; the region of interest is pooled, and the features are pooled to a suspicious lesion region; the cancer lesions are classified by a clustering distribution priori self-embedded cancer lesion classification network; and finally, visualizing is carried out on a narrow-band imaging endoscopic image,and frame selection marking is carried out on the cancer lesion by using different colors. The cancer focus of the early esophageal squamous cell carcinoma existing in the image is detected and diagnosed, the diagnosis efficiency can be effectively improved, and a doctor is assisted in obtaining higher diagnosis precision.

Description

technical field [0001] The invention belongs to the technical field of medical image processing, and in particular relates to a deep detection network for quantifying the morphological distribution of esophageal mucosa IPCLs vessels. Background technique [0002] Esophageal cancer and gastric cancer are common malignant tumors of the upper gastrointestinal tract in developing countries such as China. New cases in China account for more than 40% of the total number of cases in the world, and the morbidity and mortality are significantly higher than the world average. [10] . According to the latest statistics from the China Cancer Registry Center, new cases of esophageal cancer and gastric cancer in China rank sixth and second in the incidence of malignant tumors, respectively. The prognosis of esophageal cancer and gastric cancer is poor, and the 5-year relative survival rates are 20.9% and 27.4%, respectively, which pose a serious burden on health care [11,13-14] . Standa...

AI Technical Summary

Problems solved by technology

In this case, clinicians need to observe all the structures, which is extremely easy to cause visual fatigue. Coupled with the lack of clinical experience, after observing 5-10 visual fields, clinicians often only remember the impression "Refer to Murphy's Law" for particularly profound parts, lacking an objective and quantifiable concept, which may easily lead to misjudgment of the condition and errors in medical decision-making

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