this paper introduces a novel methodology for the segmentation of brain ms lesions in mri volumes using a new clustering algorithm named scpfcm.  scpfcm uses membership, typicality and spatial information to cluster each voxel. the proposed method relies on an initial segmentation of ms lesions in t1-w and t2-w images by applying scpfcm algorithm, and the t1 image is then used as a mask and is ...

Manual brain tumor segmentation of brain tumors from MRI is a challenging and time consuming task. Brain tumors are very difficult to segment because they have a wide range of appearance and effect on surrounding structures. Brain tumors generallyvary in size, position and image intensities (such T1 intensity, T2 intensity etc.) as seen in MRI. MRI images have overlapping intensities with norma...

Groupwise segmentation that simultaneously segments a set of images and ensures that the segmentations for the same structure of interest from different images are consistent usually can achieve better performance than segmenting each image independently. Our main contribution is that we adopt the groupwise segmentation framework to improve the performance of multi-atlas label fusion. We develo...

Combining information from multi-view images is crucial to improve the performance and robustness of automated methods for disease diagnosis. However, due non-alignment characteristics images, building correlation data fusion across views largely remain an open problem. In this study, we present TransFusion, a Transformer-based architecture merge divergent imaging using convolutional layers pow...

As information sources are usually imperfect, it is necessary to take into account their reliability in multi-source fusion tasks. In this paper, we propose a new deep framework allowing us merge multi-MR image segmentation results using the formalism of Dempster-Shafer theory while taking different modalities relative classes. The composed an encoder-decoder feature extraction module, evidenti...

Accurate segmentation of pathological regions in brain magnetic resonance images (MRI) is essential for the diagnosis and treatment tumors. Multi-modality MRIs, which offer diverse feature information, are commonly utilized tumor image segmentation. Deep neural networks have become prevalent this field; however, many approaches simply concatenate different modalities input them directly into ne...

Accurate segmentation of histopathology whole slide images (WSIs) is a crucial task for disease diagnosis and treatment planning. However, conventional automated algorithms may not always be applicable to WSI due their large size variations in tissue appearance, staining, imaging conditions. Recent advances interactive segmentation, which combines human expertise with algorithms, have shown pro...