Contrast-enhanced multiphase magnetic resonance imaging (MRI), combined with other non-contrast MRI, has become the standard approach for diagnosing focal liver lesions (FLLs). Due to the complex nature of FLLs, it is essential to automatically segment lesions and classify them from multiple MRI sequences. While sequence-specific deep learning (DL) models can be applied, general-purpose segmentation models with a unified encoder and a multitask decoder have shown great effectiveness for multitask multisequence MR analysis, particularly for organ and lesion segmentation through joint learning schemes. The key feature of such foundational integrated models is their ability to process different sequences and to achieve various segmentation tasks using the same model. By fusing the feature vectors encoded from language-based task descriptions, general segmentation models allow specific image features to be used for different segmentation tasks during decoding. Hundreds of segmentation tasks can thus be performed through one general segmentation model, with potential zero-shot capability. Building upon this concept, we propose a multitask deep learning model (MDLM) for segmentation of organs and lesions in multisequence abdominal MRI. Trained on over 15 MRI sequences per subject, our model effectively performs multiple tasks including organ segmentation, hepatic segments segmentation, vessel segmentation, and focal liver lesion segmentation. Lesion classification is also achieved using the same encoder. This approach improves the feasibility of FLL diagnosis and is integrated into our deep learning-assisted FLL diagnosis application. Experimental results demonstrate the model’s effectiveness in image segmentation, providing invaluable clinical decision support in liver imaging.