Custom implants in Total Knee Arthroplasty (TKA) could improve prosthesis’ durability and patient’s comfort, but designing such personalized implants requires a simplified and thus automatic workflow to be easily integrated in the clinical routine. A good knowledge of the shape of the patient's femur and tibia is necessary to design it, but segmentation is still today a key issue. We present here an automatic segmentation approach of the three joints of the lower limb: hip, knee and ankle, using convolutional neural networks (CNNs) on successive transverse views from CT images. Our three 2D CNNs are built on the U-net model, and their specialization each on one joint allowed us to achieve promising results presented here. This could be integrated in a TKA planning software allowing the automatic design of TKA custom implants.