## 1 Introduction
Therefore, we identified the gap and were the first to work on AI-based learning of ST features using radiographic images (easily accessible) [24; 25]. In reference to our study, a recent study was conducted on identifying ST features automatically using CBCT images [26]. The challenge of our study is working on radiographs themselves, as the characteristics of ST vary from patient to patient in the considered dataset; therefore, in our study, ST differs from regular to complex altered structures. Thus, the proposed study has grouped the dataset into three subgroups based on the complexity of the ST features: regular ST, moderately altered ST, and complex altered ST, which require the extraction of minute details. This study presents a novel hybrid segmentation pipeline for identifying the different morphological features of the Sella Turcica (ST) in lateral cephalometric radiographs. The method uses a hybrid encoder-decoder convolution structure with densely connected layers as the encoder to predict the initial mask. This predicted mask is combined with the input image and the encoder-decoder network, consisting of recurrent residual convolutional layers (RrCLs), squeeze-and-excite blocks (SEs), and an attention gate module (AGM), to provide the final improved segmentation mask by eliminating undesirable features. Additionally, a zero-shot linear classifier (ZsLC) is integrated for the accurate and distinctive classification of ST characteristics. Figure 2: Manual quantification of ST size using reference lines. The introduced method leverages the novel combination of SE, AGM, and ZsLC components, not previously studied in this context, making the segmentation process more robust, accurate, and adaptable. The hybrid model effectively captures intricate patterns, focuses on relevant regions, and seamlessly addresses the complexities present in the pre-defined ST classes, which is difficult through conventional methods. The model works on deep layers through extracting minute features and distinct between different Sella types automatically. The unique combination of different modules allows the model to achieve state-of-the-art segmentation results and shows potential for a wide range of segmentation tasks in the future. The proposed method is evaluated on two standard datasets. The first is a self-collected dataset comprising four pre-defined classes (circular, flattened, oval, and bridging ST types) and three sub-classes (regular, moderately altered, and complex altered ST). The second dataset is the publicly available IEEE ISBI 2015 challenge dataset for four-class segmentation. The proposed method outperforms the existing state-of-the-art CNN architectures in segmenting the morphological characteristics of lateral cephalometric radiographs. The study's significant contributions are as follows:  
* We introduced Sella Morphology Network (SellaMorph-Net), a novel hybrid pipeline for accurately segmenting different Sella Turcica (ST) morphological types with precise delineation of fine edges. * The approach utilised a complete CNN framework with efficient training and exceptional precision, eliminating the need for complex heuristics to enhance the network's sensitivity for focused pixels. * Attention Gating Modules (AGMs) were incorporated into our proposed method, allowing the network to concentrate on specific regions of interest (RoI) while preserving spatial accuracy and improving the feature map's quality. * The results demonstrate meaningful differentiation of ST structures using a Zero-shot linear classifier, along with an additional colour-coded layer depicting ST structures in lateral cephalometric (lat ceph) radiographic images.