The SegNet model has been shown to provide the best performance in air-tissue boundary (ATB) segmentation in real-time Magnetic Resonance Imaging (rtMRI) videos in seen subject conditions. The SegNet model uses overall binary cross entropy as the loss function. However, such a global loss function does not give enough emphasis on regions which are more prone to errors. In this work, together with global loss, we explore the use of regional loss functions which focus on areas of the contours which have been analysed as error prone in the past. Evaluation is done using global Dynamic Time Warping (DTW) distance as well as regional metrics. The regional metrics used are EVEL and VELrDTW for contour1, and ETB and TBrDTW for contour2. We show that using such combinations of regional and global losses improves the regional, as well as global, evaluation metrics. For the best combination of losses, the two regional metrics show an improvement of 37.2% and 25.3% for contour1 and 23.9% and 28.4% for contour2, over a baseline model which uses only global loss. Global DTW distance, on the other hand, improves by 11.2% for contour1 and 5.6% for contour2.