  Motivated by the need for low electron dose transmission electron microscopy
imaging, we report the optimal frame dose (i.e. $e^-/A^{2}$) range for object
detection and segmentation tasks with neural networks. The MSD-net architecture
shows promising abilities over the industry standard U-net architecture in
generalising to frame doses below the range included in the training set, for
both simulated and experimental images. It also presents a heightened ability
to learn from lower dose images. The MSD-net displays mild visibility of a Au
nanoparticle at 20-30 $e^-/A^{2}$, and converges at 200 $e^-/A^{2}$ where a
full segmentation of the nanoparticle is achieved. Between 30 and 200
$e^-/A^{2}$ object detection applications are still possible. This work also
highlights the importance of modelling the modulation transfer function when
training with simulated images for applications on images acquired with
scintillator based detectors such as the Gatan Oneview camera. A parametric
form of the modulation transfer function is applied with varying ranges of
parameters, and the effects on low electron dose segmentation is presented.
