A method for segmenting a brain image, wherein the method comprises: obtaining a group of to-be-segmented images, wherein the group of to-be-segmented images comprises a plurality of modal images of a brain; performing skull stripping according to the plurality of modal images, to obtain a mask for stripping a skull; extracting features from the plurality of modal images separately, and fusing the extracted features to obtain a fusion feature; segmenting encephalic tissues according to the fusion feature, to obtain an initial segmentation result; and fusing the mask and the initial segmentation result, to obtain a segmentation result corresponding to the group of to-be-segmented images; wherein: the plurality of modal images comprises a first modal image for tissue region segmentation, a second modal image for encephalic region identification, and a third modal image for protein lesion region identification; performing the skull stripping according to the plurality of modal images to obtain the mask for stripping the skull comprises: performing the skull stripping according to the first modal image and the second modal image, to obtain the mask for stripping the skull; and extracting the features from the plurality of modal images separately comprises: extracting the features from the first modal image, the second modal image, and the third modal image separately; and characterized in that extracting the features from the first modal image, the second modal image, and the third modal image separately comprises: fusing the first modal image and the second modal image, to obtain a fusion image; and extracting the features from the fusion image and the third modal image separately through a trained multi-branch fully convolutional network. obtaining a group of to-be-segmented images, wherein the group of to-be-segmented images comprises a plurality of modal images of a brain; performing skull stripping according to the plurality of modal images, to obtain a mask for stripping a skull; extracting features from the plurality of modal images separately, and fusing the extracted features to obtain a fusion feature; segmenting encephalic tissues according to the fusion feature, to obtain an initial segmentation result; and fusing the mask and the initial segmentation result, to obtain a segmentation result corresponding to the group of to-be-segmented images; wherein: the plurality of modal images comprises a first modal image for tissue region segmentation, a second modal image for encephalic region identification, and a third modal image for protein lesion region identification; performing the skull stripping according to the plurality of modal images to obtain the mask for stripping the skull comprises: performing the skull stripping according to the first modal image and the second modal image, to obtain the mask for stripping the skull; and extracting the features from the plurality of modal images separately comprises: extracting the features from the first modal image, the second modal image, and the third modal image separately; and characterized in that extracting the features from the first modal image, the second modal image, and the third modal image separately comprises: fusing the first modal image and the second modal image, to obtain a fusion image; and extracting the features from the fusion image and the third modal image separately through a trained multi-branch fully convolutional network. the plurality of modal images comprises a first modal image for tissue region segmentation, a second modal image for encephalic region identification, and a third modal image for protein lesion region identification; performing the skull stripping according to the plurality of modal images to obtain the mask for stripping the skull comprises: performing the skull stripping according to the first modal image and the second modal image, to obtain the mask for stripping the skull; and extracting the features from the plurality of modal images separately comprises: extracting the features from the first modal image, the second modal image, and the third modal image separately; and characterized in that extracting the features from the first modal image, the second modal image, and the third modal image separately comprises: fusing the first modal image and the second modal image, to obtain a fusion image; and extracting the features from the fusion image and the third modal image separately through a trained multi-branch fully convolutional network. fusing the first modal image and the second modal image, to obtain a fusion image; and extracting the features from the fusion image and the third modal image separately through a trained multi-branch fully convolutional network.