# Exploiting Global Structure Information to Improve Medical Image Segmentation
## 4 Experiments
### Lung Segmentation Result  
U-net [4] was utilized as a base segmentation network to perform comparative experiments involving existing frameworks such as the ACNN [12] and SRM [13] which consider the anatomical structures during training. Tables 1 and 2 summarize the detailed architectures of the segmentation network and autoencoder for this experiment, respectively. As an activation function, the rectified linear unit (ReLU) was employed. Note that the feature maps \(h\) and \(z\) should be the same size to compute the embedding loss \(L_{E}(h,z)\). The comparison targets, ACNN and SRM, were trained with the same architectures to enable a fair comparison. In general, to apply the pre-trained anatomical information to the segmentation network, the ACNN performs lower-dimensional projections of both the segmentation predictions and ground-truths based on the pre-trained autoencoder, and computes the shape regularization loss between these projections. In this experiment, we adopted the binary cross-entropy and mean squared error as the segmentation loss and shape regularization loss, respectively, as in the original study. The weight of the shape regularization loss was set as 0.01 according to the validation process. The SRM [13] is a variant of the ACNN, which introduces an auxiliary loss function, specifically, the reconstruction loss, to ensure that the outputs from the projections obtained using the pre-trained autoencoder are similar. Therefore, the objective function in the SRM consists of three loss functions, specifically, the segmentation, shape regularization, and reconstruction losses. As in the original study, we used the dice loss as the segmentation and reconstruction loss, and the binary cross-entropy as the shape regularization loss. Through the validation process, the weights for the shape regularization and reconstruction loss were set as 0.01 and 0.001, respectively. For the proposed method, we set \(\lambda\) in Equation (3) as 1.0. All the methods were trained using the Adam optimizer [29] with a learning rate of 0.0001 for 120 epochs. Histogram equalization was performed as a preprocessing step. For data augmentation, we performed brightness and contrast adjustment by setting the range from 0.8 to 1.2. The dataset was randomly split into a training, validation, and test dataset at a ratio of 65%, 15%, and 20%, respectively. To enable a rigorous evaluation, all the experiments were repeated five times, and the mean and standard deviation of the performance values were reported. Table 3 presents the average and standard deviation of the performance values over five runs, corresponding to the proposed method and comparison targets. \(\downarrow\) and \(\uparrow\) indicate that lower and higher values are better, respectively. For each experiment, the best result is expressed in boldface. As baselines, the performances corresponding to the training of only a segmentation network with (U-Net) or without data augmentation (U-Net w/o aug) are reported in the table. The results on both the datasets indicate that data augmentation enhances the segmentation performance. In addition, we observed that the methods in which the anatomical information was reflected during training, ACNN and SRM, outperformed the baselines, U-Net and U-Net w/o aug. Specifically, the ACNN and SRM exhibited an enhanced performance in terms of the distance metrics and all metrics in the case of the JSRT and MC datasets, respectively. Nevertheless, the proposed method outperformed all the methods in terms of all overlap and distance metrics on both the datasets. The comparison results indicated that in terms of the ASD, the proposed method exhibited an enhancement of 5.6% and 5.4% over the JSRT and MC datasets, respectively, against the second-best performing model SRM. This result demonstrated that the proposed method can help the segmentation network learn the global anatomical structure to be segmented by producing segmentation outputs from the anatomical feature space modeled by the autoencoder.