Development and routine implementation of deep learning algorithm for automatic brain metastases segmentation on MRI for RANO-BM criteria follow-up by Loïse Dessoude, Raphaëlle Lemaire, Romain Andres, Thomas Leleu, Alexandre G. Leclercq, Alexis Desmonts, Typhaine Corroller, Amirath Fara Orou-Guidou, Luca Laduree, Loic Le Henaff, Joëlle Lacroix, Alexis Lechervy, Dinu Stefan, Aurélien Corroyer-Dulmont

“…There was a high degree of overlap between the AI and the doctor's segmentation, with a mean DICE score of 0.77. The diameter and volume of the BM lesions were found to be concordant between the AI and the reference segmentation. …”
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A novel multimodality anthropomorphic phantom enhances compliance with quality assurance guidelines for magnetic resonance imaging in radiotherapy by Meshal Alzahrani, David A Broadbent, Irvin Teh, Bashar Al-Qaisieh, Emily Johnstone, Richard Speight

“…Both phantoms achieved target registration errors (TREs) below 0.97 mm and dice similarity coefficient (DSC) values above 0.9, meeting guidelines. …”
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Proof of concept of fully automated adaptive workflow for head and neck radiotherapy treatments with a conventional linear accelerator by Gaia Muti, Marco M. J. Felisi, Angelo F. Monti, Chiara Carsana, Roberto Pellegrini, Edoardo Salmeri, Mauro Palazzi, Paola E. Colombo

“…An analysis of the timing for the different steps is carried out to assess its clinical applicability.ResultThe dice of the five HU layer structures range between 0.66 and 0.99. …”
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Automatic segmentation model and machine learning model grounded in ultrasound radiomics for distinguishing between low malignant risk and intermediate-high malignant risk of adnex... by Lu Liu, Wenjun Cai, Feibo Zheng, Hongyan Tian, Yanping Li, Ting Wang, Xiaonan Chen, Wenjing Zhu

“…Results The FCN ResNet101 demonstrated the highest segmentation performance for adnexal masses (Dice similarity coefficient: 89.1%). Support vector machine achieved the best AUC (0.961, 95% CI: 0.925–0.996). …”
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Transformers for Neuroimage Segmentation: Scoping Review by Maya Iratni, Amira Abdullah, Mariam Aldhaheri, Omar Elharrouss, Alaa Abd-alrazaq, Zahiriddin Rustamov, Nazar Zaki, Rafat Damseh

“…The most frequent evaluation metric was the Dice score (n=63, 94.03%). Studies generally reported increased segmentation accuracy and the ability to model both local and global features in brain images. …”
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Quantifying the tumour vasculature environment from CD-31 immunohistochemistry images of breast cancer using deep learning based semantic segmentation by Tristan Whitmarsh, Wei Cope, Julia Carmona-Bozo, Roido Manavaki, Stephen-John Sammut, Ramona Woitek, Elena Provenzano, Emma L. Brown, Sarah E. Bohndiek, Ferdia A. Gallagher, Carlos Caldas, Fiona J. Gilbert, Florian Markowetz

“…Results Using two-way cross-validation, we show that vessels were accurately segmented, with Dice scores of 0.875 and 0.856, and were accurately identified, with F1 scores of 0.777 and 0.748. …”
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Probabilistic nested model selection in pharmacokinetic analysis of DCE-MRI data in animal model of cerebral tumor by Hassan Bagher-Ebadian, Stephen L. Brown, Mohammad M. Ghassemi, Prabhu C. Acharya, Indrin J. Chetty, Benjamin Movsas, James R. Ewing, Kundan Thind

“…The K-SOM PNMS’s estimation for the leaky tumor regions were strongly similar (Dice-Similarity-Coefficient, DSC = 0.774 [CI: 0.731–0.823], and 0.866 [CI: 0.828–0.912] for Models 2 and 3, respectively) to their respective NMS regions. …”
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A deep learning approach for automatic 3D segmentation of hip cartilage and labrum from direct hip MR arthrography by Malin Kristin Meier, Ramon Andreas Helfenstein, Adam Boschung, Andreas Nanavati, Adrian Ruckli, Till D. Lerch, Nicolas Gerber, Bernd Jung, Onur Afacan, Moritz Tannast, Klaus A. Siebenrock, Simon D Steppacher, Florian Schmaranzer

“…Model performance was assessed with six evaluation metrics including Dice similarity coefficient (DSC). In addition, model performance was tested on an external dataset (40 patients) with a 3D T2-weighted sequence from a different institution. …”
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Deep unsupervised clustering for prostate auto-segmentation with and without hydrogel spacer by Hengrui Zhao, Biling Wang, Michael Dohopolski, Ti Bai, Steve Jiang, Dan Nguyen

“…CLIP-UNet with cluster information achieved a Dice score of 86.2% compared to 84.4% from the baseline UNet. …”
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Uncertainty quantification in multi-parametric MRI-based meningioma radiotherapy target segmentation by Lana Wang, Zhenyu Yang, Zhenyu Yang, Dominic LaBella, Zachary Reitman, John Ginn, Jingtong Zhao, Justus Adamson, Kyle Lafata, Kyle Lafata, Kyle Lafata, Evan Calabrese, John Kirkpatrick, Chunhao Wang

“…Regarding segmentation performance, SPU-Net demonstrated comparable results to a traditional U-Net in sensitivity (0.758 vs. 0.746), Dice similarity coefficient (0.760 vs. 0.742), reduced mean Hausdorff distance (mHD) (0.612 cm vs 0.744 cm), and reduced 95% Hausdorff distance (HD95) (2.682 cm vs 2.912 cm). …”
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Fully automatic reconstruction of prostate high-dose-rate brachytherapy interstitial needles using two-phase deep learning-based segmentation and object tracking algorithms by Mohammad Mahdi Moradi, Zahra Siavashpour, Soheib Takhtardeshir, Eman Showkatian, Ramin Jaberi, Reza Ghaderi, Bahram Mofid, Farzad Taghizadeh-Hesary

“…The total number of needles in these slices of CT images was 8764, of which the employed pix2pix network was able to segment 98.72 % (8652 of total). Dice Similarity Coefficient (DSC) and IoU (Intersection over Union) between the network output and the ground truth were 0.95 and 0.90, respectively. …”
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Deep learning-based synthetic CT for dosimetric monitoring of combined conventional radiotherapy and lattice boost in large lung tumors by Hongwei Zeng, Xiangyu E, Minghe Lv, Su Zeng, Yue Feng, Wenhao Shen, Wenhui Guan, Yang Zhang, Ruping Zhao, Jingping Yu

“…After rigid and hybrid deformable registration of sCT and pCT, the mean distance-to-agreement was 0.80 ± 0.18 mm, and the mean Dice similarity coefficient was 0.97 ± 0.01. Monitoring dose accumulation over 20 CRT fractions showed an increase in high-dose regions of the GTV (P < 0.05) and a reduction in low-dose regions (P < 0.05). …”
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High-performance laser speckle contrast image vascular segmentation without delicate pseudo-label reliance by Shenglan Yao, Huiling Wu, Suzhong Fu, Shuting Ling, Kun Wang, Hongqin Yang, Yaqin He, Xiaolan Ma, Xiaofeng Ye, Xiaofei Wen, Qingliang Zhao

“…Furthermore, we demonstrate that our method is more robust and effective in mitigating performance degradation than traditional segmentation approaches on diverse style data sets, even when confronted with unfamiliar data. Importantly, the dice similarity coefficient exceeded 85% in a rat experiment. …”
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Enhancing deep learning methods for brain metastasis detection through cross-technique annotations on SPACE MRI by Tassilo Wald, Benjamin Hamm, Julius C. Holzschuh, Rami El Shafie, Andreas Kudak, Balint Kovacs, Irada Pflüger, Bastian von Nettelbladt, Constantin Ulrich, Michael Anton Baumgartner, Philipp Vollmuth, Jürgen Debus, Klaus H. Maier-Hein, Thomas Welzel

“…Multiple DL methods were developed with NAQ or HAQ using either SPACE or MRPAGE images and evaluated on their detection performance using positive predictive value (PPV), sensitivity, and F1 score and on their delineation performance using volumetric Dice similarity coefficient, PPV, and sensitivity on one internal and four additional test datasets (660 patients). …”
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TECHNOLOGY OF LASER CUTTING OF SILICON WAFERS INTO ORGANIC LIGHT-EMITTING DIODE CHIPS by V. S. Kondratenko, V. I. Ivanov

“…Data for rating the merit of the surfaces of chips after the laser controlled thermocracking and dicing, namely, the availability and size of the chipping, as well as surface roughness are presented and compared. …”
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Preliminary Studies on the Development and Evaluation of Instant Pounded Yam from <i>Dioscorea alata</i> by Abiodun A Adeola, Bolanle O Otegbayo, Sola Ogunnoiki

“…Instant poundo yam flour was prepared from D. alata by peeling, dicing and immersing yam tubers in sodium metabisulphite solution (800 ppm for 20 min). …”
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