GCT-UNET: U-Net Image Segmentation Model for a Small Sample of Adherent Bone Marrow Cells Based on a Gated Channel Transform Module

Abstract

Pathological diagnosis is considered to be declarative and authoritative. However, reading pathology slides is a challenging task. Different parts of the section are taken and read for different purposes and with different focuses, which further adds difficulty to the pathologist’s diagnosis. In recent years, the deep neural network has made great progress in the direction of computer vision and the main approach to image segmentation is the use of convolutional neural networks, through which the spatial properties of the data are captured. Among a wide variety of different network structures, one of the more representative ones is UNET with encoder and decoder structures. The biggest advantage of traditional UNET is that it can still perform well with a small number of samples, but because the information in the feature map is lost in the downsampling process of UNET, and a large amount of spatially accurate detailed information is lost in the decoding part. This makes it difficult to complete accurate segmentation of cell images with dense numbers and high adhesion. For this reason, we propose a new network structure based on UNET, which can be used to segment cell images by aggregating the global contextual information between different channels and assigning different weights to the corresponding channels through the gated adaptive mechanism, we improve the performance of UNET in the cell segmentation task and consider the use of unsupervised segmentation methods for secondary segmentation of the predicted results of our model, and the final results obtained are tested to meet the needs of the readers.

Structure of a semantic segmentation-based defect detection network for laser cladding infrared images

“…The authors also demonstrate that U-net can still exert a good segmentation effect on datasets with fewer samples. At the same time, this conclusion is proved and improved to be applied in several papers [25,26]. Therefore, many optimization improvement algorithms are based on U-net.…”

Segmentación morfológica y clasificación de niveles para la retinopatía diabética e hipertensiva mediante imágenes oftálmicas y redes convolucionales

“…Furthermore, rather than opting for commonly used datasets such as DRIVE or CHASEDB1, it was selected also a diabetic dataset that deviates from the conventional approach by lacking pre-existing masks. Finally, the selection over the use of UNet unlike others was because this is specially adapted for the segmentation of biomedical objects, specially is justified by the efficiency in training with limited data sets, which is essential in our context (61).…”