  Semantic segmentation of brain tumours is a fundamental task in medical image
analysis that can help clinicians in diagnosing the patient and tracking the
progression of any malignant entities. Accurate segmentation of brain lesions
is essential for medical diagnosis and treatment planning. However, failure to
acquire specific MRI imaging modalities can prevent applications from operating
in critical situations, raising concerns about their reliability and overall
trustworthiness. This paper presents a novel multi-modal approach for brain
lesion segmentation that leverages information from four distinct imaging
modalities while being robust to real-world scenarios of missing modalities,
such as T1, T1c, T2, and FLAIR MRI of brains. Our proposed method can help
address the challenges posed by artifacts in medical imagery due to data
acquisition errors (such as patient motion) or a reconstruction algorithm's
inability to represent the anatomy while ensuring a trade-off in accuracy. Our
proposed regularization module makes it robust to these scenarios and ensures
the reliability of lesion segmentation.
