[1] GARC′IA-LORENZO D, FRANCIS S, NARAYANAN S, et al. Review of automatic segmentation methodsof multiple sclerosis white matter lesions on conventionalmagnetic resonance imaging [J]. Medical ImageAnalysis, 2013, 17(1): 1-18.
[2] SARITHA S, PRABHA N A. A comprehensive review:Segmentation of MRI images — brain tumor [J]. InternationalJournal of Imaging Systems and Technology,2016, 26(4): 295-304.
[3] WADHWA A, BHARDWAJ A, VERMA V S. A reviewon brain tumor segmentation of MRI images [J].Magnetic Resonance Imaging, 2019, 61: 247-259.
[4] WEEDA M M, BROUWER I, DE VOS M L, et al.Comparing lesion segmentation methods in multiplesclerosis: Input from one manually delineated subjectis sufficient for accurate lesion segmentation [J]. NeuroImage:Clinical, 2019, 24: 102074.
[5] BERNAL J, KUSHIBAR K, ASFAW D S, et al. Deepconvolutional neural networks for brain image analysison magnetic resonance imaging: A review [J]. ArtificialIntelligence in Medicine, 2019, 95: 64-81.
[6] KERVADEC H, BOUCHTIBA J, DESROSIERS C, etal. Boundary loss for highly unbalanced segmentation[J]. Medical Image Analysis, 2021, 67: 101851.
[7] CHEN C, QIN C, QIU H Q, et al. Deep learning forcardiac image segmentation: A review [J]. Frontiers inCardiovascular Medicine, 2020, 7: 25.
[8] IS?IN A, DIREKOˇGLU C, S?AH M. Review of MRIbasedbrain tumor image segmentation using deeplearning methods [J]. Procedia Computer Science,2016, 102: 317-324.
[9] JAAFRA Y, LAURENT J L, DERUYVER A, et al.Reinforcement learning for neural architecture search:A review [J]. Image and Vision Computing, 2019, 89:57-66.
[10] MAKROPOULOS A, COUNSELL S J, RUECKERTD. A review on automatic fetal and neonatal brainMRI segmentation [J]. NeuroImage, 2018, 170: 231-248.
[11] SCHMIDHUBER J. Deep learning in neural networks:An overview [J]. Neural Networks, 2015, 61: 85-117.
[12] JADON S. A survey of loss functions for semanticsegmentation [EB/OL]. [2020-07-16]. https://arxiv.org/pdf/2006.14822.pdf.
[13] MA J. Segmentation loss odyssey [EB/OL]. [2020-07-16]. https://arxiv.org/pdf/2005.13449.pdf.
[14] MILLETARI F, NAVAB N, AHMADI S A. V-net:Fully convolutional neural networks for volumetricmedical image segmentation [C]//2016 Fourth InternationalConference on 3D Vision (3DV). Stanford,California, USA: IEEE, 2016: 565-571.
[15] DROZDZAL M, VORONTSOV E, CHARTRAND G,et al. The importance of skip connections in biomedicalimage segmentation [M]//CARNEIRO G, MATEUSD, PETER L, et al. Deep learning and data labelingfor medical applications. Cham: Springer, 2016: 179-187.
[16] FIDON L, LI W Q, GARCIA-PERAZA-HERRERA LC, et al. Generalised wasserstein Dice score for imbalancedmulti-class segmentation using holistic convolutionalnetworks [M]//CRIMI A, BAKAS S, KUIJFB, et al. Brainlesion: Glioma, multiple sclerosis,stroke and traumatic brain injuries. Cham, Switzerland:Springer, 2018: 64-76.
[17] REZATOFIGHI H, TSOI N, GWAK J Y, et al. Generalizedintersection over union: A metric and a loss forbounding box regression [C]//2019 IEEE/CVF Conferenceon Computer Vision and Pattern Recognition(CVPR). Long Beach, CA, USA: IEEE, 2019: 658-666.
[18] DOLZ J, DESROSIERS C, AYED I B. 3D fully convolutionalnetworks for subcortical segmentation in MRI:A large-scale study [J]. NeuroImage, 2018, 170: 456-470.
[19] GUIZARD N, COUP?P, FONOV V S, et al. Rotationinvariantmulti-contrast non-local means for MS lesionsegmentation [J]. NeuroImage: Clinical, 2015, 8: 376-389.
[20] HARMOUCHE R, SUBBANNA N K, COLLINS D L,et al. Probabilistic multiple sclerosis lesion classificationbased on modeling regional intensity variabilityand local neighborhood information [J]. IEEE Transactionson Biomedical Engineering, 2015, 62(5): 1281-1292.
[21] STYNER M, LEE J, CHIN B, et al. 3D segmentationin the clinic: A grand challenge II: MS lesion segmentation[J]. MIDAS Journal, 2008, 2008: 1-6.
[22] WONG K C L, MORADI M, TANG H, et al. 3Dsegmentation with exponential logarithmic loss forhighly unbalanced object sizes [M]//FRANGI A F,SCHNABEL J A, DAVATZIKOS C, et al. Medicalimage computing and computer assisted intervention— MICCAI 2018. Cham, Switzerland: Springer, 2018:612-619.
[23] LUCAS C, KEMMLING A, MAMLOUK A M, etal. Multi-scale neural network for automatic segmentationof ischemic strokes on acute perfusion images[C]//2018 IEEE 15th International Symposium onBiomedical Imaging (ISBI 2018). Washington, DC,USA: IEEE, 2018: 1118-1121.
[24] WANG ZW, SMITH C D, LIU J D. Ensemble of multisizedFCNs to improve white matter lesion segmentation[M]//SHI Y H, SUK H I, LIUM X. Machine learningin medical imaging. Cham, Switzerland: Springer,2018: 223-232.
[25] KARIMI D, SALCUDEAN S E. Reducing the hausdorffdistance in medical image segmentation with convolutionalneural networks [J]. IEEE Transactions onMedical Imaging, 2020, 39(2): 499-513.
[26] YANG D, ROTH H, WANG X S, et al. Enhancingforeground boundaries for medicalimage segmentation [EB/OL]. [2020-07-16].https://arxiv.org/pdf/2005.14355.pdf.
[27] ODA H, ROTH H R, CHIBA K, et al. BESNet:Boundary-enhanced segmentation of cells inhistopathological images [M]//FRANGI A F,SCHNABEL J A, DAVATZIKOS C, et al. Medicalimage computing and computer assisted intervention— MICCAI 2018. Cham, Switzerland: Springer, 2018:228-236.
[28] SUDRE C H, LIWQ, VERCAUTEREN T, et al. GeneralisedDice overlap as a deep learning loss functionfor highly unbalanced segmentations [M]//CARDOSOM J, ARBEL T, CARNEIRO G, et al. Deep Learningin medical image analysis and multimodal learningfor clinical decision support. Cham, Switzerland:Springer, 2017: 240-248.
[29] TAGHANAKI S A, ZHENG Y F, ZHOU S K, et al.Combo loss: Handling input and output imbalancein multi-organ segmentation [J]. Computerized MedicalImaging and Graphics, 2019, 75: 24-33.
[30] SALEHI S S M, ERDOGMUS D, GHOLIPOUR A.Tversky loss function for image segmentation using 3Dfully convolutional deep networks [M]//WANG Q, SHIY H, SUK H I, et al. Machine Learning in MedicalImaging. Cham, Switzerland: Springer, 2017: 379-387.
[31] RONNEBERGER O, FISCHER P, BROX T. U-net:Convolutional networks for biomedical image segmentation[C]//NAVAB N, HORNEGGER J, WELLS WM, et al. Medical image computing and computerassistedintervention—MICCAI 2015. Cham, Switzerland:Springer, 2015: 234-241.
[32] WU Z F, SHEN C H, VAN DEN HENGELA. Bridging category-level and instance-level semanticimage segmentation [EB/OL]. [2020-07-16].https://arxiv.org/pdf/1605.06885.pdf.
[33] LIN T Y, GOYAL P, GIRSHICK R, et al. Focalloss for dense object detection [C]//2017 IEEE InternationalConference on Computer Vision (ICCV ).Venice, Italy: IEEE, 2017: 2980-2988.
[34] WANG P, CHUNG A C S. Focal Dice lossand image dilation for brain tumor segmentation[M]//STOYANOV D, TAYLOR Z, CARNEIRO G, etal. Deep learning in medical image analysis and multimodallearning for clinical decision support. Cham,Switzerland: Springer, 2018: 119-127.
[35] ZHOU Y J, HUANG W J, DONG P, et al. D-UNet:A dimension-fusion U shape network for chronicstroke lesion segmentation [J]. IEEE/ACM Transactionson Computational Biology and Bioinformatics,2019. https://doi.org/10.1109/TCBB.2019.2939522(published online).
[36] ASLANI S, MURINO V, DAYAN M, et al. Scannerinvariant multiple sclerosis lesion segmentation fromMRI [C]//2020 IEEE 17th International Symposiumon Biomedical Imaging (ISBI). Lowa City, IA, USA:IEEE, 2020: 781-785.
[37] HASHEMIS R, SALEHI S S M, ERDOGMUS D, et al.Asymmetric loss functions and deep densely-connectednetworks for highly-imbalanced medical image segmentation:Application to multiple sclerosis lesion detection[J]. IEEE Access, 2019, 7: 1721-1735.
[38] XUE Y Z, FARHAT F G, BOUKRINA O, et al. Amulti-path 2.5 dimensional convolutional neural networksystem for segmenting stroke lesions in brain MRIimages [J]. NeuroImage: Clinical, 2020, 25: 102118.
[39] LI H L, PARIKH N A, WANG J H, et al. Objectiveand automated detection of diffuse white matter abnormalityin preterm infants using deep convolutionalneural networks [J]. Frontiers in Neuroscience, 2019,13: 610.
[40] GROS C, DE LEENER B, BADJI A, et al. Automaticsegmentation of the spinal cord and intramedullarymultiple sclerosis lesions with convolutional neural networks[J]. NeuroImage, 2019, 184: 901-915.
[41] RACHMADI M F, VALD′ES-HERN′ANDEZ M D C,AGAN M L F, et al. Segmentation of white matter hyperintensitiesusing convolutional neural networks withglobal spatial information in routine clinical brain MRIwith none or mild vascular pathology [J]. ComputerizedMedical Imaging and Graphics, 2018, 66: 28-43.
[42] NAIR T, PRECUP D, ARNOLD D L, et al. Exploringuncertainty measures in deep networks for multiplesclerosis lesion detection and segmentation [J]. MedicalImage Analysis, 2020, 59: 101557.
[43] XU B T, CHAI Y Q, GALARZA C M, et al. Orchestralfully convolutional networks for small lesionsegmentation in brain MRI [C]//2018 IEEE 15th InternationalSymposium on Biomedical Imaging (ISBI2018). Washington, DC, USA: IEEE, 2018: 889-892.
[44] NACEUR M B, AKIL M, SAOULI R, et al. Fully automaticbrain tumor segmentation with deep learningbasedselective attention using overlapping patches andmulti-class weighted cross-entropy [J]. Medical ImageAnalysis, 2020, 63: 101692.
[45] KUZINA A, EGOROV E, BURNAEV E. Bayesiangenerative models for knowledge transfer in MRI semanticsegmentation problems [J]. Frontiers in Neuroscience,2019, 13: 844.
[46] GHAFFARI M, SOWMYA A, OLIVER R. Automatedbrain tumor segmentation using multimodalbrain scans: A survey based on models submitted tothe BraTS 2012—2018 Challenges [J]. IEEE Reviewsin Biomedical Engineering, 2020, 13: 156-168.
[47] KUMAR A, UPADHYAY N, GHOSAL P, et al.CSNet: A new DeepNet framework for ischemic strokelesion segmentation [J]. Computer Methods and Programsin Biomedicine, 2020, 193: 105524.
[48] WANG G T, SONG T, DONG Q, et al. Automatic ischemicstroke lesion segmentation from computed tomographyperfusion images by image synthesis andattention-based deep neural networks [J]. Medical ImageAnalysis, 2020, 65: 101787.
[49] HU S Y, WENG W H, LU S L, et al. Multimodalvolume-aware detection and segmentation for brainmetastases radiosurgery [M]//NGUYEN D, XING L,JIANG S. Artificial Intelligence in Radiation Therapy.Cham, Switzerland: Springer, 2019: 61-69.
[50] MAIER O, MENZE B H, VON DER GABLENTZ J,et al. ISLES 2015: A public evaluation benchmark forischemic stroke lesion segmentation from multispectralMRI [J]. Medical Image Analysis, 2017, 35: 250-269.
[51] C?IC?EK ¨ O, ABDULKADIR A, LIENKAMP S S, etal. 3D U-Net: Learning dense volumetric segmentationfrom sparse annotation [M]//OURSELIN S,JOSKOWICZ L, SABUNCU M R, et al. Medical imagecomputing and computer-assisted intervention —MICCAI 2016. Cham, Switzerland: Springer, 2016:424-432.
[52] MYRONENKO A. 3D MRI brain tumor segmentationusing autoencoder regularization [M]//CRIMI A,BAKAS S, KUIJF H, et al. Brainlesion: Glioma, multiplesclerosis, stroke and traumatic brain injuries.Cham, Switzerland: Springer, 2018: 311-320.
[53] MOSTAPHA M, STYNER M. Role of deep learningin infant brain MRI analysis [J]. Magnetic ResonanceImaging, 2019, 64: 171-189.
[54] WARING J, LINDVALL C, UMETON R. Automatedmachine learning: Review of the state-of-the-art andopportunities for healthcare [J]. Artificial Intelligencein Medicine, 2020, 104: 101822
