Abstract: A retinal layer quantification system includes an image capturing unit and a processor electrically connected to the image capturing unit. The image capturing unit is configured to capture a target optical coherence tomographic image of a subject. The processor stores a program including a target image pre-processing module, a retinal layer auto-segmentation model, a target image enhancement module, a layer thickness detection module and a layer area detection module. The program detects a retinal layer thickness and a retinal layer area of the subject when the program is executed by the processor.

Abstract: A method for multi-link operation (MLO) is provided. The method for MLO may be applied to an apparatus. The method for MLO may include the following steps. A multi-chip controller of the apparatus may assign different data to a plurality of chips of the apparatus, wherein each chip corresponds to one link of multi-links. Each chip may determine whether transmission of the assigned data has failed. A first chip of the chips may transmit the assigned data to an access point (AP) in response to the first chip determining that the transmission of the assigned data has not failed.

Abstract: An establishing method of a retinal layer thickness detection model includes following steps. A reference database is obtained, and an image pre-processing step, a feature selecting step, a training step and a confirming step are performed. The reference database includes reference optical coherence tomographic images. In the image pre-processing step, the reference optical coherence tomographic images are duplicated and cell segmentation lines of retinal layers are marked to obtain control optical coherence tomographic images. In the feature selecting step, the reference optical coherence tomographic images are analyzed to obtain reference image features. The training step is to train with the reference image features and obtain the retinal layer thickness detection model.

Abstract: An establishing method of a retinal layer thickness detection model includes following steps. A reference database is obtained, and an image pre-processing step, a feature selecting step, a training step and a confirming step are performed. The reference database includes reference optical coherence tomographic images. In the image pre-processing step, the reference optical coherence tomographic images are duplicated and cell segmentation lines of retinal layers are marked to obtain control optical coherence tomographic images. In the feature selecting step, the reference optical coherence tomographic images are analyzed to obtain reference image features. The training step is to train with the reference image features and obtain the retinal layer thickness detection model.