CN107749061A - Based on improved full convolutional neural networks brain tumor image partition method and device - Google Patents

Based on improved full convolutional neural networks brain tumor image partition method and device Download PDF

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CN107749061A
CN107749061A CN201710812236.9A CN201710812236A CN107749061A CN 107749061 A CN107749061 A CN 107749061A CN 201710812236 A CN201710812236 A CN 201710812236A CN 107749061 A CN107749061 A CN 107749061A
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邢波涛
李锵
关欣
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Tianjin University
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformations in the plane of the image
    • G06T3/40—Scaling of whole images or parts thereof, e.g. expanding or contracting
    • G06T3/4007—Scaling of whole images or parts thereof, e.g. expanding or contracting based on interpolation, e.g. bilinear interpolation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/50—Image enhancement or restoration using two or more images, e.g. averaging or subtraction
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10088—Magnetic resonance imaging [MRI]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20024—Filtering details
    • G06T2207/20032—Median filtering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20212—Image combination
    • G06T2207/20221—Image fusion; Image merging
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30016—Brain
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30096—Tumor; Lesion

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Abstract

The present invention relates to medicine equipment, to propose a kind of improved full convolutional neural networks, realize the machine segmentation of brain tumor nuclear magnetic resonance image, avoid the defects of inefficient and unstable existing for artificial segmentation, quick, reliable brain tumor segmentation result is provided, so as to provide accurate foundation for the diagnosis of brain tumor, treatment and operation guiding.Therefore, the technical solution adopted by the present invention is, it is as follows based on improved full convolutional neural networks brain tumor image partition method, step：1) image is chosen；2) full convolutional neural networks FCN models are built；3) segmentation result is tested, after FCN model trainings are good, the prediction of knub position and boundary sizes is carried out to any one brain tumor image using the model of training, and segmentation result is evaluated using corresponding evaluation index, preferably to improve FCN models.Present invention is mainly applied to nuclear-magnetism image procossing occasion.

Description

Based on improved full convolutional neural networks brain tumor image partition method and device
Technical field
The present invention relates to medicine equipment, specifically, is related to based on improved full convolutional neural networks brain tumor picture segmentation Method and device.
Background technology
Brain is most important part in human body, but the brain tumor incidence of disease is in rising trend in recent years, shows according to investigations, The brain tumor number being diagnosed in the U.S. has just increased 23,000 people newly within only 2015.The World Health Organization is according to lesion degree brain Tumour is divided into five grades, and brain tumor is broadly divided into the benign tumours such as benign tumour and the class of malignant tumour two, meningioma and passed through It is generally possible to get well after operative treatment, and the malignant tumour such as glioma and collagen knurl is because its intractable is difficult to cure, again It is referred to as the cancer of the brain.Magnetic resonance imaging (Magnetic Resonance Imaging, MRI) has Noninvasive, can be in patient Do not receive to provide the information such as shape, size and location in the case of high ionization radiation, and there is good soft tissue contrast, Had been to be concerned by more and more people in diagnosis, treatment and the operation guiding of brain tumor.But because brain tumor is complex-shaped, size There is randomness with position, the factors such as type difference is big cause that there is presently no the need that a kind of partitioning algorithm disclosure satisfy that clinic Will, real-time is also unable to reach requirement, and the result of different expert's manual segmentation brain tumor images also has very big difference, and manually Cost is higher.Therefore, the brain tumor segmenting system for studying a set of full-automatic, accuracy height and strong robustness is extremely to be necessary 's.
Have many algorithms at present to be successfully applied in the segmentation of brain tumor image, mainly include the segmentation side based on region Method, the dividing method based on fuzzy clustering and convolutional neural networks method etc..Dividing method based on region growing is first in mesh A seed point is chosen in mark region, then the pixel that surrounding has same nature is incorporated into seed region, until not similar Untill the pixel aggregation of property, for region-growing method, generally existing is undesirable to the segmentation of image shadow region to ask Topic；The dividing method of fuzzy clustering is that image pixel is classified by its similarity so that in small distance between similar individual And it is in larger distance between inhomogeneity individual, for clustering algorithm, generally only consider the independent information of each pixel and neglect Somewhat like the spatial information between vegetarian refreshments, it is affected by noise larger and the segmentation knot of continuum can not be obtained to finally result in algorithm Fruit；Image partition method based on convolutional neural networks (Convolutional Neural Network, CNN) is to a picture During element classification, it is used to train and predict using a block of pixels around the pixel as CNN input, then constantly slides Window, the window slided every time gives CNN and carries out identification and classification, so as to realize the segmentation of whole image.But the brain based on CNN swells Knurl dividing method can make computer storage overhead become big along with the increase of sliding window, and adjacent pixel blocks substantially weight Multiple, convolution is calculated one by one for each block of pixels, computationally there is very big repeatability, it is most important that CNN convolution filter Ripple device can make it that the partitioning boundary of brain tumor is unintelligible along with very big receptive field and maximum pond layer.
2015, Jonathan Long et al. by full convolutional neural networks (Fully Convolution Network, FCN image, semantic segmentation field) is successfully applied to, and achieves significant effect.The network can be to corresponding to position in image The each pixel put is classified, so as to realize the task of image segmentation.Full articulamentum in traditional CNN is changed chemical conversion by FCN Convolutional layer one by one.In lower Fig. 1,5 layers are convolutional layers before traditional CNN structures, and the 6th layer and the 7th layer is one long respectively The one-dimensional vector for 4096 is spent, the 8th layer is probability of the length for 1000 one-dimensional vector, respectively corresponding 1000 classifications；And FCN Be expressed as convolutional layer by this 3 layers, the size of convolution kernel (port number, wide, high) be respectively (4096,1,1), (4096,1,1) and (1000,1,1), by such convolutional layer, finally just generate one " feature vector chart ".All layers are all volumes in FCN Lamination, without full articulamentum, so referred to as full convolutional neural networks.
The characteristic pattern that Network Capture profound FCN arrives is abstract and coarse, and these characteristic patterns have passed through substantial amounts of maximum pond Change layer, the information such as edge, the position of target object are have lost significantly in characteristic pattern；And shallow-layer network only have passed through minority Characteristic pattern caused by maximum pond layer Pooling is specific and fine, and what is learnt is the feature of some regional areas, these features Figure can be to reflect the position of target object and marginal information., can be with so in order to further be optimized to segmentation result The characteristic pattern of network shallow-layer is combined with the characteristic pattern of deep layer, this is the jump structure of FCN networks, whole to improve The segmentation precision of image.
The content of the invention
For overcome the deficiencies in the prior art, the present invention is directed to propose a kind of improved full convolutional neural networks, realize that brain swells The machine segmentation of knurl nuclear magnetic resonance image, avoids the defects of inefficient and unstable existing for artificial segmentation, there is provided quick, reliable Brain tumor segmentation result, so as to for the diagnosis of brain tumor, treatment and operation guide accurate foundation is provided.It is therefore, of the invention The technical scheme of use is, as follows based on improved full convolutional neural networks brain tumor image partition method, step：
1) image is chosen：First to magnetic resonance MRI brain tumor fluid attented inversion recoveries FLAIR, spin-spin relaxation T2 And
T1 enhancing T1C images use medium filtering denoising, then linear scale fusion are carried out to three kinds of mode, so as to more preferable
Ground is split by brain tumor；
2) full convolutional neural networks FCN models are built：Using the brain tumor image after merging well as training sample, Zhuan Jiafen True value label of the pixel as training sample of result figure is cut, then carries out the training of FCN models；
3) segmentation result is tested, after FCN model trainings are good, any one brain tumor image is entered using the model of training The prediction of row knub position and boundary sizes, and segmentation result is evaluated using corresponding evaluation index, so as to preferably Improve FCN models.
Test in segmentation result step, input data is by Pooling1, Pooling2, Pooling 3, Pooling After 4 and Pooling 5 maximum pond layer, can respectively obtain the size of artwork 1/2,1/4 size, 1/8 size, 1/16 size and The feature vector chart of 1/32 size.The feature of 4th convolution kernel is carried out deconvolution to the figure after last Upsamping1 Upsamping2 subsidiary details, equivalent to one Interpolation Process, then the feature of the 3rd convolution kernel to Upsamping2 just now it Rear image carries out deconvolution Upsamping3 subsidiary details again, finally again the feature of the 2nd convolution kernel to just now Image after Upsamping3 carries out deconvolution Upsamping4 subsidiary details again, consequently, it is possible to the feature of deep layer network Scheme to be complementary to one another with the characteristic pattern of shallow-layer network, be finally added and obtain an of a relatively high segmentation figure of precision.
FCN carries out up-sampling Upsamping, interpolation point using warp lamination to the feature vector chart of last convolutional layer P 4 adjoint point coordinates are respectively Q11=(x1,y1), Q21=(x2,y1), Q12=(x1,y2), Q22=(x2,y2), first in X side First time linear interpolation calculating is carried out upwards, is obtained：
Then linear interpolation is carried out in the Y direction, is obtained：
Formula (1) and formula (2) are updated into formula can obtain interpolation point P coordinate value f (x, y)：
By so operation, input picture identical size finally will be returned to by the feature vector chart in pond, So as to produce a prediction to each pixel, while the spatial information in original input picture is remained, finally up-sampled Characteristic pattern in classified pixel-by-pixel, so as to realize image split.
Based on improved full convolutional neural networks brain tumor image segmenting device, gone by magnetic resonance MRI machine, medium filtering Make an uproar device, linearity ratio Fusion Module, capture card, computer；
Linearity ratio Fusion Module is used to the image for choosing tri- kinds of mode of FLAIR, T2 and T1C carrying out linear scale Fusion；
Image caused by magnetic resonance MRI machine successively through medium filtering denoising device, linearity ratio Fusion Module, adopt Truck enters computer；
Computer installation has structure FCN model modules, test segmentation result module；
Structure FCN model modules are used for, using the brain tumor image after merging well as training sample, expert's segmentation result figure True value label of the pixel as training sample, carry out the training of FCN models, construct FCN models；
Test segmentation result module is used for, after FCN model trainings are good, using the model of training to any one brain tumor Image carries out the prediction of knub position and boundary sizes, and segmentation result is evaluated using corresponding evaluation index, so as to Preferably improve FCN models.
The features of the present invention and beneficial effect are：
The present invention combines newest full convolutional neural networks image partition method, and network structure is improved, and proposes FCN-4s network structures, and the brain tumor image of various modes is merged, so as to realize the Accurate Segmentation of brain tumor image. Compared with the method for the traditional classical such as convolutional neural networks, its advantage is mainly reflected in：
1) it is practical：What CNN was inputted every time is a block of pixels, the probability of simply each pixel of output, and FCN It is a kind of end-to-end end-to-end neutral net, it can input the picture of arbitrary size, and can export correspondingly sized Split picture, it is more practical；
2) real-time is good：For FCN without going to judge the classification of each pixel as CNN sliding windows, it is a kind of segmentation of Pixel-level Network, the problem of avoiding the repetition storage due to being brought using block of pixels and calculate convolution, calculator memory expense is small, from And image can be made to split in real time；
3) accuracy is high：The conventional methods such as CNN simply carry out judging to divide to a pixel every time when splitting image Class, and the feature vector chart that FCN is finally exported remains the spatial information in original input picture, to considering during pixel classifications Relation between pixel, so it can preferably obtain the border of brain tumor image, improves the degree of accuracy of segmentation.
Brief description of the drawings：
The patient with brain tumors of the types of Fig. 1 tetra-；
The segmentation result figure of Fig. 2 expert；
The improved full convolutional neural networks segmentation MRI brain tumor algorithm flow charts of Fig. 3；
Fig. 4 FCN figures compared with CNN difference；
The bilinear interpolation up-sampled in Fig. 5 FCN；
The full convolutional neural networks structure chart of Fig. 6 MRI images；
Fig. 7 brain tumors segmentation result and evaluation figure.
Embodiment
The present invention combines medical image and deep learning algorithm, completes the segmentation of brain tumor nuclear magnetic resonance image. This full automatic brain tumor image segmentation will produce important influence in Medical Imaging.
For traditional convolutional neural networks in the defects of image segmentation, the present invention proposes a kind of improved full convolution Neutral net, and be successfully applied in the segmentation of brain tumor nuclear magnetic resonance image, avoid inefficient existing for artificial segmentation And the defects of unstable.Quick, reliable brain tumor segmentation result is provided using brand-new deep learning algorithm, so as to be swollen for brain Diagnosis, treatment and the operation guiding of knurl provide accurate foundation.
To achieve these goals, the present invention adopts the following technical scheme that：
1) image is chosen.MRI brain tumors image itself is of low quality, certain noise, the image of three kinds of mode be present The uncorrelated and complementary information in part is provided for the segmentation of brain tumor, so in being used first to FLAIR, T2 and T1C image Value filtering denoising, linear scale fusion then is carried out to three kinds of mode, preferably to split to brain tumor.
2) FCN models are built.Using the brain tumor image after merging well as training sample, the pixel of expert's segmentation result figure True value label of the point as training sample, then carries out the training of FCN models.
3) segmentation result is tested.After FCN model trainings are good, any one brain tumor image is entered using the model of training The prediction of row knub position and boundary sizes, and segmentation result is evaluated using corresponding evaluation index, so as to preferably Improve FCN models.
The segmentation of the invention for effectively supporting brain tumor nuclear magnetic resonance image is deep learning in medical image segmentation side One important application in face, the mode to expert's manual measurement are to supplement well.
The invention will be further described with example below in conjunction with the accompanying drawings.
1) image is chosen
Patient with brain tumors MRI imaging be three-dimensional multi-spectral imaging, generally include Flair (fluid attented inversion recovery), Four kinds of modality images such as T1 (SPIN-LATTICE RELAXATION), T2 (spin-spin relaxation) and T1C (T1 enhancings), Fig. 1 (a) be T1 into Picture, it is simple to operate, the structural analysis of brain tumor is frequently utilized for, but image quality is poor, can not provide more detailed image information. Fig. 1 (b) is T1C imagings, is T1 Enhanced Imaging, and it, which can compare, clearly displays cerebral tissue structure, and due to Hypertrophic Contrast build-up caused by the barrier breakdown of brain tumor region so that the brain tumor border in T1 Enhanced Imagings becomes brighter, very Easily distinguish tumour and capsule becomes region.Fig. 1 (c) is T2 imagings, and area of edema is more bright compared to other imaging mode, but brain The feature of spinal fluid and tumor region is difficult to differentiate between, and is unfavorable for lesion segmentation.Fig. 1 (d) is that FLAIR is imaged, white matter of brain in this mode Contrast is low, but tumor region is remarkably reinforced with normal structure contrast, and area of edema border is obvious, eliminates the shadow of cerebrospinal fluid Ring, the tumour that the ventricles of the brain are taken up with convex surface or brain ditch show apparent, is advantageous to the display of tumor region, is to split brain at present to swell The maximally effective mode of knurl image.The characteristics of being imaged thus according to different modalities, the present invention is in order to farthest obtain brain tumor The position in region and size boundary information, the image for choosing tri- kinds of mode of FLAIR, T2 and T1C is carried out to the fusion of linear scale.
2) FCN models are built
FCN of the present invention convolutional coding structure can be shown in See Figure 6.Input data is in the maximum pond (Pooling) by 5 times Afterwards, the feature vector chart of the size of artwork 1/2,1/4 size, 1/8 size, 1/16 size and 1/32 size can be respectively obtained.Such as Fruit is directly up-sampled the operation of (Upsamping) to the feature vector chart of 1/32 size, because such operation reduction Picture is only the feature in the 5th convolution kernel, is limited to the feature that precision problem cannot be gone back among original image well, because This iteration forward herein.The feature of 4th convolution kernel is carried out convolution benefit again to the image after last Upsamping Details (equivalent to one Interpolation Process) is filled, that is, obtains the FCN-16s network architecture.If again the feature of the 3rd convolution kernel Image after being up-sampled to second carries out deconvolution subsidiary details again, i.e., can obtain the FCN-8s network architecture.Finally Image after the feature of the 2nd convolution kernel is up-sampled to third time again carries out deconvolution subsidiary details again, that is, obtains The FCN-4s network architecture.Consequently, it is possible to the characteristic pattern of the characteristic pattern of deep layer network and shallow-layer network can be complementary to one another, finally Addition can obtain the of a relatively high segmentation figure picture of a precision.
Specifically, FCN is up-sampled using warp lamination to the feature vector chart of last convolutional layer (Upsamping), the rough image so as to make resolution ratio low returns to the resolution ratio of artwork.Upsamping is mainly pair View data carry out bilinear interpolation operation, by the use of interpolation point around 4 adjoint points gray value Weighted Interpolation as the point gray scale Value, it can be decomposed into quadratic one-dimensional linear interpolation.Can be shown in See Figure 4, interpolation point P 4 adjoint point coordinates are respectively Q11= (x1,y1), Q21=(x2,y1), Q12=(x1,y2), Q22=(x2,y2).Carry out first time linear interpolation meter in the X direction first Calculate, obtain：
Then linear interpolation is carried out in the Y direction, is obtained：
Formula (1) and formula (2) are updated into formula can obtain interpolation point P coordinate value f (x, y)：
By so operation, input picture identical size finally will be returned to by the feature vector chart in pond, So as to produce a prediction to each pixel, while the spatial information in original input picture is remained, finally upper Classified pixel-by-pixel in the characteristic pattern of sampling, so as to realize that image is split.
3) segmentation result is tested
The present invention by FCN-16s, FCN-8s with it is proposed that the FCN-4s network architectures carried out contrast test, specific point Cutting result and test index can be shown in See Figure 7.

Claims (4)

1. one kind is based on improved full convolutional neural networks brain tumor image partition method, it is characterized in that, step is as follows：
1) image is chosen：First to magnetic resonance MRI brain tumor fluid attented inversion recoveries FLAIR, spin-spin relaxation T2 and T1 Enhancing T1C images use medium filtering denoising, then linear scale fusion are carried out to three kinds of mode, so as to preferably to brain tumor Split；
2) full convolutional neural networks FCN models are built：Split knot using the brain tumor image after merging well as training sample, expert True value label of the pixel of fruit figure as training sample, then carry out the training of FCN models；
3) segmentation result is tested, after FCN model trainings are good, any one brain tumor image is swollen using the model of training Knurl position and the prediction of boundary sizes, and segmentation result is evaluated using corresponding evaluation index, preferably to improve FCN models.
2. as claimed in claim 1 based on improved full convolutional neural networks brain tumor image partition method, it is characterized in that, survey Try in segmentation result step, input data is passing through Pooling1, Pooling2, Pooling3, Pooling4 and Pooling5 Maximum pond layer after, the spy of the size of artwork 1/2,1/4 size, 1/8 size, 1/16 size and 1/32 size can be respectively obtained Levy vectogram.The feature of 4th convolution kernel is carried out deconvolution Upsamping2 benefits to the figure after last Upsamping1 Details is filled, equivalent to one Interpolation Process, then the feature of the 3rd convolution kernel is carried out to the image after Upsamping2 just now Deconvolution Upsamping3 subsidiary details again, finally again the feature of the 2nd convolution kernel to Upsamping3 just now after Image carries out deconvolution Upsamping4 subsidiary details again, consequently, it is possible to the spy of the characteristic pattern of deep layer network and shallow-layer network Sign figure can be complementary to one another, and be finally added and obtained an of a relatively high segmentation figure of precision.
3. as claimed in claim 1 based on improved full convolutional neural networks brain tumor image partition method, it is characterized in that, FCN carries out up-sampling Upsamping, interpolation point P 4 neighbours to the feature vector chart of last convolutional layer using warp lamination Point coordinates is respectively Q11=(x1,y1), Q21=(x2,y1), Q12=(x1,y2), Q22=(x2,y2), carry out in the X direction first First time linear interpolation calculates, and obtains：
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Then linear interpolation is carried out in the Y direction, is obtained：
<mrow> <mi>f</mi> <mrow> <mo>(</mo> <mi>P</mi> <mo>)</mo> </mrow> <mo>&amp;ap;</mo> <mfrac> <mrow> <msub> <mi>y</mi> <mn>2</mn> </msub> <mo>-</mo> <mi>y</mi> </mrow> <mrow> <msub> <mi>y</mi> <mn>2</mn> </msub> <mo>-</mo> <msub> <mi>y</mi> <mn>1</mn> </msub> </mrow> </mfrac> <mi>f</mi> <mrow> <mo>(</mo> <msub> <mi>R</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> <mo>+</mo> <mfrac> <mrow> <mi>y</mi> <mo>-</mo> <msub> <mi>y</mi> <mn>1</mn> </msub> </mrow> <mrow> <msub> <mi>y</mi> <mn>2</mn> </msub> <mo>-</mo> <msub> <mi>y</mi> <mn>1</mn> </msub> </mrow> </mfrac> <mi>f</mi> <mrow> <mo>(</mo> <msub> <mi>R</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>3</mn> <mo>)</mo> </mrow> </mrow>
Formula (1) and formula (2) are updated into formula can obtain interpolation point P coordinate value f (x, y)：
<mrow> <mtable> <mtr> <mtd> <mrow> <mi>f</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>&amp;ap;</mo> <mfrac> <mrow> <mi>f</mi> <mrow> <mo>(</mo> <msub> <mi>Q</mi> <mn>11</mn> </msub> <mo>)</mo> </mrow> </mrow> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mn>2</mn> </msub> <mo>-</mo> <msub> <mi>x</mi> <mn>1</mn> </msub> <mo>)</mo> <mo>(</mo> <msub> <mi>y</mi> <mn>2</mn> </msub> <mo>-</mo> <msub> <mi>y</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> </mfrac> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mn>2</mn> </msub> <mo>-</mo> <mi>x</mi> <mo>)</mo> </mrow> <mrow> <mo>(</mo> <msub> <mi>y</mi> <mn>2</mn> </msub> <mo>-</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>+</mo> <mfrac> <mrow> <mi>f</mi> <mrow> <mo>(</mo> <msub> <mi>Q</mi> <mn>21</mn> </msub> <mo>)</mo> </mrow> </mrow> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mn>2</mn> </msub> <mo>-</mo> <msub> <mi>x</mi> <mn>1</mn> </msub> <mo>)</mo> <mo>(</mo> <msub> <mi>y</mi> <mn>2</mn> </msub> <mo>-</mo> <msub> <mi>y</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> </mfrac> <mrow> <mo>(</mo> <mi>x</mi> <mo>-</mo> <msub> <mi>x</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> <mrow> <mo>(</mo> <msub> <mi>y</mi> <mn>2</mn> </msub> <mo>-</mo> <mi>y</mi> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>+</mo> <mfrac> <mrow> <mi>f</mi> <mrow> <mo>(</mo> <msub> <mi>Q</mi> <mn>12</mn> </msub> <mo>)</mo> </mrow> </mrow> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mn>2</mn> </msub> <mo>-</mo> <msub> <mi>x</mi> <mn>1</mn> </msub> <mo>)</mo> <mo>(</mo> <msub> <mi>y</mi> <mn>2</mn> </msub> <mo>-</mo> <msub> <mi>y</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> </mfrac> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mn>2</mn> </msub> <mo>-</mo> <mi>x</mi> <mo>)</mo> </mrow> <mrow> <mo>(</mo> <mi>y</mi> <mo>-</mo> <msub> <mi>y</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> <mo>+</mo> <mfrac> <mrow> <mi>f</mi> <mrow> <mo>(</mo> <msub> <mi>Q</mi> <mn>22</mn> </msub> <mo>)</mo> </mrow> </mrow> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mn>2</mn> </msub> <mo>-</mo> <msub> <mi>x</mi> <mn>1</mn> </msub> <mo>)</mo> <mo>(</mo> <msub> <mi>y</mi> <mn>2</mn> </msub> <mo>-</mo> <msub> <mi>y</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> </mfrac> <mrow> <mo>(</mo> <mi>x</mi> <mo>-</mo> <msub> <mi>x</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> <mrow> <mo>(</mo> <mi>y</mi> <mo>-</mo> <msub> <mi>y</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> </mtable> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>4</mn> <mo>)</mo> </mrow> </mrow>
By so operation, input picture identical size finally will be returned to by the feature vector chart in pond, so as to A prediction is produced to each pixel, while remains the spatial information in original input picture, finally in the spy of up-sampling Classified pixel-by-pixel in sign figure, so as to realize that image is split.
4. one kind is based on improved full convolutional neural networks brain tumor image segmenting device, it is characterized in that, set by magnetic resonance MRI Standby, medium filtering denoising device, linearity ratio Fusion Module, capture card, computer；
Linearity ratio Fusion Module is used to the image for choosing tri- kinds of mode of FLAIR, T2 and T1C carrying out melting for linear scale Close；
Image caused by magnetic resonance MRI machine is successively through medium filtering denoising device, linearity ratio Fusion Module, capture card Into computer；
Computer installation has structure FCN model modules, test segmentation result module；
Structure FCN model modules are used for, using the brain tumor image after merging well as training sample, the picture of expert's segmentation result figure True value label of the vegetarian refreshments as training sample, the training of FCN models is carried out, constructs FCN models；
Test segmentation result module is used for, after FCN model trainings are good, using the model of training to any one brain tumor image The prediction of knub position and boundary sizes is carried out, and segmentation result is evaluated using corresponding evaluation index, so as to more preferable Improve FCN models in ground.
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