Automatic　computerized　segmentation　of　fetal　head　from　ultrasound　images　and　head　circumference　(HC)　biometric　measurement　is　still　challenging,　due　to　the　inherent　characteristics　of　fetal　ultrasound　images　at　different　semesters　of　pregnancy.　In　this　paper,　we　proposed　a　new　deep　learning　method　for　automatic　fetal　ultrasound　image　segmentation　and　HC　biometry:　deeply　supervised　attention-gated　(DAG)　V-Net,　which　incorporated　the　attention　mechanism　and　deep　supervision　strategy　into　V-Net　models.　In　addition,　multi-scale　loss　function　was　introduced　for　deep　supervision.　The　training　set　of　the　HC18　Challenge　was　expanded　with　data　augmentation　to　train　the　DAG　V-Net　deep　learning　models.　The　trained　models　were　used　to　automatically　segment　fetal　head　from　two-dimensional　ultrasound　images,　followed　by　morphological　processing,　edge　detection,　and　ellipse　fitting.　The　fitted　ellipses　were　then　used　for　HC　biometric　measurement.　The　proposed　DAG　V-Net　method　was　evaluated　on　the　testing　set　of　HC18　(n　=　355),　in　terms　of　four　performance　indices:　Dice　similarity　coefficient　(DSC),　Hausdorff　distance　(HD),　HC　difference　(DF),　and　HC　absolute　difference　(ADF).　Experimental　results　showed　that　DAG　V-Net　had　a　DSC　of　97.93%,　a　DF　of　0.09　±　2.45 mm,　an　AD　of　1.77　±　1.69 mm,　and　an　HD　of　1.29　±　0.79 mm.　The　proposed　DAG　V-Net　method　ranks　fifth　among　the　participants　in　the　HC18　Challenge.　By　incorporating　the　attention　mechanism　and　deep　supervision,　the　proposed　method　yielded　better　segmentation　performance　than　conventional　U-Net　and　V-Net　methods.　Compared　with　published　state-of-the-art　methods,　the　proposed　DAG　V-Net　had　better　or　comparable　segmentation　performance.　The　proposed　DAG　V-Net　may　be　used　as　a　new　method　for　fetal　ultrasound　image　segmentation　and　HC　biometry.　The　code　of　DAG　V-Net　will　be　made　available　publicly　on　https://github.com/xiaojinmao-code/　.