Purpose: Accurately segmenting curvilinear structures, for example, retinal blood vessels or nerve fibers, in the medical image is essential to the clinical diagnosis of many diseases. Recently, deep learning has become a popular technology to deal with the image segmentation task, and it has obtained remarkable achievement. However, the existing methods still have many problems when segmenting the curvilinear structures in medical images, such as losing the details of curvilinear structures, producing many false-positive segmentation results. To mitigate these problems, we propose a novel end-to-end curvilinear structure segmentation network called Curv-Net.
Methods: Curv-Net is an effective encoder-decoder architecture constructed based on selective kernel (SK) and multibidirectional convolutional LSTM (multi-Bi-ConvLSTM). To be specific, we first employ the SK module in the convolutional layer to adaptively extract the multi-scale features of the input image, and then we design a multi-Bi-ConvLSTM as the skip concatenation to fuse the information learned in the same stage and propagate the feature information from the deep stages to the shallow stages, which can enable the feature captured by Curv-Net to contain more detail information and high-level semantic information simultaneously to improve the segmentation performance.
Results: The effectiveness and reliability of our proposed Curv-Net are verified on three public datasets: two color fundus datasets (DRIVE and CHASE_DB1) and one corneal nerve fiber dataset (CCM-2). We calculate the accuracy (ACC), sensitivity (SE), specificity (SP), Dice similarity coefficient (Dice), and area under the receiver (AUC) for the DRIVE and CHASE_DB1 datasets. The ACC, SE, SP, Dice, and AUC of the DRIVE dataset are 0.9629, 0.8175, 0.9858, 0.8352, and 0.9810, respectively. For the CHASE_DB1 dataset, the values are 0.9810, 0.8564, 0.9899, 0.8143, and 0.9832, respectively. To validate the corneal nerve fiber segmentation performance of the proposed Curv-Net, we test it on the CCM-2 dataset and calculate Dice, SE, and false discovery rate (FDR) metrics. The Dice, SE, and FDR achieved by Curv-Net are 0.8114 ± 0.0062, 0.8903 ± 0.0113, and 0.2547 ± 0.0104, respectively.
Conclusions: Curv-Net is evaluated on three public datasets. Extensive experimental results demonstrate that Curv-Net outperforms the other superior curvilinear structure segmentation methods.
Keywords: U-shape networks; curvilinear structure segmentation; deep learning; multi-Bi-ConvLSTM; selective kernel module.
© 2022 American Association of Physicists in Medicine.