  The various speech sounds of a language are obtained by varying the shape and
position of the articulators surrounding the vocal tract. Analyzing their
variations is crucial for understanding speech production, diagnosing speech
disorders and planning therapy. Identifying key anatomical landmarks of these
structures on medical images is a pre-requisite for any quantitative analysis
and the rising amount of data generated in the field calls for an automatic
solution. The challenge lies in the high inter- and intra-speaker variability,
the mutual interaction between the articulators and the moderate quality of the
images. This study addresses this issue for the first time and tackles it by
means by means of Deep Learning. It proposes a dedicated network architecture
named Flat-net and its performance are evaluated and compared with eleven
state-of-the-art methods from the literature. The dataset contains midsagittal
anatomical Magnetic Resonance Images for 9 speakers sustaining 62 articulations
with 21 annotated anatomical landmarks per image. Results show that the
Flat-net approach outperforms the former methods, leading to an overall Root
Mean Square Error of 3.6 pixels/0.36 cm obtained in a leave-one-out procedure
over the speakers. The implementation codes are also shared publicly on GitHub.
