This paper presents a comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images. It is based on results from the "MICCAI 2007 Grand Challenge" workshop, where 16 teams evaluated their algorithms on a common database. A collection of 20 clinical images with reference segmentations was provided to train and tune algorithms in advance. Participants were also allowed to use additional proprietary training data for that purpose. All teams then had to apply their methods to 10 test datasets and submit the obtained results. Employed algorithms include statistical shape models, atlas registration, level-sets, graph-cuts and rule-based systems. All results were compared to reference segmentations five error measures that highlight different aspects of segmentation accuracy. All measures were combined according to a specific scoring system relating the obtained values to human expert variability. In general, interactive methods reached higher average scores than automatic approaches and featured a better consistency of segmentation quality. However, the best automatic methods (mainly based on statistical shape models with some additional free deformation) could compete well on the majority of test images. The study provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.
Objective: We aimed to develop and validate a deep learning system for fully automated segmentation of abdominal muscle and fat areas on computed tomography (CT) images. Materials and Methods: A fully convolutional network-based segmentation system was developed using a training dataset of 883 CT scans from 467 subjects. Axial CT images obtained at the inferior endplate level of the 3rd lumbar vertebra were used for the analysis. Manually drawn segmentation maps of the skeletal muscle, visceral fat, and subcutaneous fat were created to serve as ground truth data. The performance of the fully convolutional network-based segmentation system was evaluated using the Dice similarity coefficient and cross-sectional area error, for both a separate internal validation dataset (426 CT scans from 308 subjects) and an external validation dataset (171 CT scans from 171 subjects from two outside hospitals). Results: The mean Dice similarity coefficients for muscle, subcutaneous fat, and visceral fat were high for both the internal (0.96, 0.97, and 0.97, respectively) and external (0.97, 0.97, and 0.97, respectively) validation datasets, while the mean cross-sectional area errors for muscle, subcutaneous fat, and visceral fat were low for both internal (2.1%, 3.8%, and 1.8%, respectively) and external (2.7%, 4.6%, and 2.3%, respectively) validation datasets. Conclusion: The fully convolutional network-based segmentation system exhibited high performance and accuracy in the automatic segmentation of abdominal muscle and fat on CT images.
Necroptosis, caspase‐independent programmed necrosis, has emerged as a therapeutic target to make dying cancer cells stimulants for antitumor immune responses. The clinical translations exploiting necroptosis, however, have been limited since most cancer cells downregulate receptor‐interacting protein kinase 3 (RIPK3) as a key enzyme for necroptosis. Herein, nanobubbles (NBs) that can trigger RIPK3‐independent necroptosis, facilitating cell‐membrane rupture via the acoustic cavitation effect are reported. The NBs, imbibing perfluoropentane as the gas precursor, are prepared using an amphiphilic polymer conjugate, composed of PEGylated carboxymethyl dextran as the hydrophilic backbone and chlorin e6 as the hydrophobic sonosensitizer. When exposed to ultrasound, the NBs efficiently promote the release of biologically active damage‐associated molecular patterns by inducing burst‐mediated cell‐membrane disintegration. Consequently, the necroptosis‐inducible NBs significantly improve antitumor immunity by maturation of dendritic cells and activation of CD8+ cytotoxic T cells both in vitro and in vivo. In addition, the combination of NBs and immune checkpoint blockade leads to complete regression of the primary tumor and beneficial therapeutic activity against metastatic tumors in an RIPK3‐deficient CT26 tumor‐bearing mouse model. Overall, the innovative NB that causes immunogenic cell death of cancer via RIPK3‐independent necroptosis is a promising enhancer for cancer immunotherapy.
The conventional chemotherapeutic agents, used for cancer chemotherapy, have major limitations including non-specificity, ubiquitous biodistribution, low concentration in tumor tissue, and systemic toxicity. In recent years, owing to their unique features, polymeric nanoparticles have been widely used for the target-specific delivery of drugs in the body. Although polymeric nanoparticles have addressed a number of important issues, the bioavailability of drugs at the disease site, and especially upon cellular internalization, remains a challenge. A polymer nanocarrier system with a stimuli-responsive property (e.g., pH, temperature, or redox potential), for example, would be amenable to address the intracellular delivery barriers by taking advantage of pH, temperature, or redox potentials. With a greater understanding of the difference between normal and pathological tissues, there is a highly promising role of stimuli-responsive nanocarriers for drug delivery in the future. In this review, we highlighted the recent advances in different types of stimuli-responsive polymers for drug delivery.
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