Deep learning models performed very well in many medical image analysis tasks. However, the majority of these results had been obtained on carefully selected datasets. At the same time, the real clinical flow of Computed Tomography studies often contains series with different properties. We address a particular discrepancy related to a much larger scanning interval, e.g., a single series for thorax, abdomen, and pelvis. We propose to use 1D body organ detection for coarse organ localization on thorax-abdomen CT scans. Localized segments, containing volumes of interests, could be further processed by a heavier task-specific network. We convert 3D CT images into multi-channel 2D coronal images, thus drastically decreasing the dimensionality of the data. We next train a conventional U-net like architecture to solve the task of body part regression and build simple threshold rules to localize lungs along the coronal plane. Additionally, this approach allows for the detection of organs only partially presented in the image. Our network was trained on 20 thousand thorax-abdomen volume segments and validated on three separate datasets. It shows high localization accuracy, stability across datasets and processes a high-resolution CT volume in no more than 200 ms.