Current Methods in Medical Image Segmentation

Dear Colleagues,

Image segmentation is a key step in medical imaging, in assisting early disease detection, diagnosis, monitoring treatment, and follow-up. Segmentation is a challenging task, due to noise, lack of contrast, and object variability in medical images. Decade after decade, research on automatic or semi-automatic image segmentation has continuously provided the medical expert with increasingly powerful and time-saving algorithms. In this regard, the breakthrough of deep convolutional neural networks (CNNs) has focused almost all research efforts in medical image segmentation in recent years. CNNs are now the state of the art in this field. Medical images raise specific issues, which have been addressed with novel backbone architectures, network blocks, frameworks, and loss functions. The annotation of medical images has also an important cost, which has led researchers to design methodologies with various levels of supervision (label-efficient machine learning), including weakly and semi-supervised learning or to investigate transfer learning. This Special Issue will present review and original articles on medical image segmentation with CNNs, targeting various modalities and pathologies. We invite submissions presenting research on topics including, but not limited to, the following: CNN architecture and frameworks, novel loss functions, weakly and semi-supervised learning, multi-task and transfer learning, extensive experimental validation, integration of imaging and clinical data, and the interpretability and explainability of segmentation CNN.

Prof. Dr. Caroline Petitjean
Guest Editor

Manuscript Submission Information

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