Gliomas, primary brain tumors arising from glial cells, can be effectively identified using Magnetic Resonance Imaging (MRI), a widely employed diagnostic tool in clinical settings. Accurate glioma segmentation, which is crucial for diagnosis and surgical intervention, can be achieved by integrating multiple MRI modalities that offer complementary information. However, limited access to multiple modalities in certain clinical contexts often results in suboptimal performance of glioma segmentation methods. This study introduces a novel generalized knowledge distillation framework designed to transfer multimodal knowledge from a teacher model to a unimodal student model via two distinct distillation strategies: segmentation graph distillation and cascade region attention distillation. The former enables the student to replicate the teacher’s softened output, whereas the latter facilitates extraction and learning of region feature information at various levels within the teacher model. Our evaluation of the proposed distillation strategies using the BraTS 2018 dataset confirms their superior performance in unimodal segmentation contexts compared with existing methods.

Interactive medical image segmentation based on human-in-the-loop machine learning is a novel paradigm that draws on human expert knowledge to assist medic

The multimodal segmentation of medical images is essential for clinical applications as it allows medical professionals to detect anomalies, monitor treatment effectiveness, and make informed therapeutic decisions. However, existing segmentation methods depend on paired images of modalities, which may not always be available in practical scenarios, thereby limiting their applicability. To address this challenge, current approaches aim to align modalities or generate missing modality images without a ground truth, which can introduce irrelevant texture details. In this paper, we propose the energy-basedsemantic augmented segmentation (ESAS) model, which employs the energy of latent semantic features from a supporting modality to enhance the segmentation performance on unpaired query modality data. The proposed ESAS model is a lightweight and efficient framework suitable for most unpaired multimodal image-learning tasks. We demonstrate the effectiveness of our ESAS model on the MM-WHS 2017 challenge dataset, where it significantly improved Dice accuracy for cardiac segmentation on CT volumes. Our results highlight the potential of the proposed ESAS model to enhance patient outcomes in clinical settings by providing a promising approach for unpaired multimodal medical image segmentation tasks.

The Segmentation Anything Model (SAM) has recently emerged as a foundation model for addressing image segmentation. Owing to the intrinsic complexity of medical images and the high annotation cost, the medical image segmentation (MIS) community has been encouraged to investigate SAM's zero-shot capabilities to facilitate automatic annotation. Inspired by the extraordinary accomplishments of interactive medical image segmentation (IMIS) paradigm, this paper focuses on assessing the potential of SAM's zero-shot capabilities within the IMIS paradigm to amplify its benefits in the MIS domain. Regrettably, we observe that SAM's vulnerability to prompt forms (e.g., points, bounding boxes) becomes notably pronounced in IMIS. This leads us to develop a framework that adaptively offers suitable prompt forms for human experts. We refer to the framework above as temporally-extended prompts optimization (TEPO) and model it as a Markov decision process, solvable through reinforcement learning. Numerical experiments on the standardized benchmark BraTS2020 demonstrate that the learned TEPO agent can further enhance SAM's zero-shot capability in the MIS context.