Acquiring ultrasound images of suspected lesion areas allows radiologists to monitor the cancer development of patients. The goal of this paper is to provide an automatic lesion segmentation tool for assisting them on the analysis of ultrasound images, by relying on recent neural network methods. Specifically, we perform a comparative study for the segmentation of 348 ultrasound image pairs acquired in 19 centers across France, displaying different tumor types. We show that, with a careful hyperparameter tuning, U-net outperforms other state-of-the-art networks, reaching a Dice coefficient of 0.929. We then propose to introduce group convolution into U-net architecture. This leads to a lightweight network named Lighter U-net @128 that achieves comparable segmentation performance with obviously reduced model size, hence paving the way for an embedded integration within hospital environment. We made our code publicly available for reproducibility purpose.