Abstract: The present disclosure is to provide a computer-aided cell segmentation method for determining cellular Nuclear-to-Cytoplasmic ratio, which comprises acts of obtaining a cytological image using non-invasive in vivo biopsy technique; performing a nuclei segmentation process to identify a position and a contour of each of identified nuclei in the cytological image; performing a cytoplasmic process with an improved active contour model to obtain a cytoplasmic region for each identified nucleus based; and determine a cellular Nuclear-to-Cytoplasmic ratio based on the obtained nucleus and cytoplasmic regions.

Abstract: A system for determining a colonoscopy image is disclosed. The system includes a bowel preparation evaluation unit, an image processing unit electrically or communicatively connected to the bowel evaluation unit, and a convolutional neural network unit electrically or communicatively connected to the image processing unit. The bowel preparation evaluation unit determines whether a bowel in the colonoscopy image is clean. The image processing unit receives the colonoscopy image determined to have a clean bowel and outputted by the bowel preparation evaluation unit, and performs an image process on the colonoscopy image with clean bowel to adjust a contrast thereof. The convolutional neural network unit comprises a convolutional neural network and in a working mode, receives and determines whether the processed colonoscopy image is a cecal image, a non-cecal image, or an indeterminable colonoscopy image.

Abstract: The present disclosure is to provide a computer-aided cell segmentation method for determining cellular Nuclear-to-Cytoplasmic ratio, which comprises acts of obtaining a cytological image using non-invasive in vivo biopsy technique; performing a nuclei segmentation process to identify a position and a contour of each of identified nuclei in the cytological image; performing a cytoplasmic process with an improved active contour model to obtain a cytoplasmic region for each identified nucleus based; and determine a cellular Nuclear-to-Cytoplasmic ratio based on the obtained nucleus and cytoplasmic regions.

Abstract: A system for determining a colonoscopy image is disclosed. The system includes a bowel preparation evaluation unit, an image processing unit electrically or communicatively connected to the bowel evaluation unit, and a convolutional neural network unit electrically or communicatively connected to the image processing unit. The bowel preparation evaluation unit determines whether a bowel in the colonoscopy image is clean. The image processing unit receives the colonoscopy image determined to have a clean bowel and outputted by the bowel preparation evaluation unit, and performs an image process on the colonoscopy image with clean bowel to adjust a contrast thereof. The convolutional neural network unit comprises a convolutional neural network and in a working mode, receives and determines whether the processed colonoscopy image is a cecal image, a non-cecal image, or an indeterminable colonoscopy image.