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Amniotic Fluid Volume (AFV) is a crucial fetal biomarker when diagnosing specific fetal abnormalities. This study proposes a novel Convolutional Neural Network (CNN) model, AFNet, for segmenting amniotic fluid (AF) to facilitate clinical AFV evaluation. AFNet was trained and tested on a manually segmented and radiologist-validated AF dataset. AFNet outperforms ResUNet++ by using efficient feature mapping in the attention block and transposing convolutions in the decoder. Our experimental results show that AFNet achieved a mean Intersection over Union (mIoU) of 93.38% on our dataset, thereby outperforming other state-of-the-art models. While AFNet achieves performance scores similar to those of the UNet++ model, it does so while utilizing merely less than half the number of parameters. By creating a detailed AF dataset with an improved CNN architecture, we enable the quantification of AFV in clinical practice, which can aid in diagnosing AF disorders during gestation. [ABSTRACT FROM AUTHOR]

Retinal vessel segmentation can obtain rich ocular information which is important for the diagnosis of fundus diseases. To address the problems of existing segmentation methods such as poor capillary segmentation and incorrect segmentation of pathological information, an AFNet vessel segmentation network combining location attention, semantic aggregation module and multi-scale feature fusion module is proposed. Add positional attention to the feature codec block of the network for modeling global dependencies and reducing intra-class inconsistencies, the multiscale feature fusion module is used in the last layer of the coding part to extract multiscale feature information to solve the difficult problem of large variation of retinal vessel width and size, and the designed semantic aggregation module can fully utilize the contextual semantic information to improve the segmentation accuracy of capillaries. Extensive experiments are conducted on three publicly available fundus image databases DRIVE, STARE and CHASE_DB1, and the results show that AFNet can effectively improve the accuracy of retinal vessel segmentation and achieve better comprehensive performance compared with other methods. [ABSTRACT FROM AUTHOR]