CN105574859A - Liver tumor segmentation method and device based on CT (Computed Tomography) image - Google Patents
Liver tumor segmentation method and device based on CT (Computed Tomography) image Download PDFInfo
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Abstract
The invention provides a liver tumor segmentation method and device based on a CT (Computed Tomography) image. The method comprises the following steps: performing Gaussian denoising on CT image data of a liver, converting the denoised CT image data into standardized data of which a gray average is 0 and a variance is 1, and performing down-sampling operation; extracting a lesion slice and a normal tissue slice from a gold standard image of the CT image of the liver, and classifying the lesion slice and the normal tissue slice into a positive sample and a negative sample; constructing a multi-level depth convolutional neural network, training a model through a stochastic gradient descent to obtain a network model, and acquiring a coarse segmentation binary image of a tumor and a pixel-classification probability image through a classifier; performing morphological erosion operation on the coarse segmentation binary image of the tumor to obtain a foreground image needed by graph cut, performing subtraction operation on the binary image of a liver and the coarse segmentation binary image of the tumor, and performing the morphological erosion operation to obtain a background image corresponding to normal tissues of the liver; and constructing an undirected graph, and obtaining a finial segmentation region of the tumor through a graph cut optimization algorithm.
Description
Technical field
The invention belongs to field of medical image processing, particularly relate to a kind of liver neoplasm dividing method based on CT image and device.
Background technology
Liver maintains the important and Functional tissue of complexity of human life activity, and hepatic disease is multiple, and pathology kind is many, and the incidence of disease is high.Computing machine emission computed tomography (ComputedTomography, CT) image has become one of conventional means important in clinical diagnosis, is the important detection methods of liver diseases.Current liver tumour treatment means mainly comprises tumor resection, intervention, radiation therapy etc., and tumor resection is wherein the most effective therapeutic modality.These treatment meanss all need the information such as quantity, position, size and shape accurately understanding tumour in the preoperative, contribute to the formulation of liver neoplasm therapeutic scheme, but tumour individual difference is large, liver neoplasm and liver parenchyma unclear boundaries, the position of tumour, size, shape, gray scale and texture are different, are difficult to work out a kind of general lesion segmentation algorithm.Segmentation manually needs to have anatomical knowledge and experience, and artificial subjectivity is strong, needs cost plenty of time and energy.Because tumor boundaries is fuzzy, the factors such as performance otherness is large, most of liver segmentation method cannot reach clinical requirement precision.
Existing full-automatic dividing liver neoplasm method, main flow process manually carries out feature extraction, feature selecting, design category device to training data, forecast model is obtained by supervised learning or unsupervised learning, according to this model, test data is predicted, characteristic extraction procedure calculated amount is large, consuming time many, and the feature that can choose is to a great extent by experience and fortune.
Summary of the invention
The technical problem to be solved in the present invention is to overcome the limitation that conventional segmentation methods hand-designed extracts feature.
In order to achieve the above object, the embodiment of the present invention provides a kind of liver neoplasm dividing method based on CT image, comprising: step 1, carries out Gauss except making an uproar to the CT view data of liver, being translated into gray average is 0, and variance is the standardized data of 1 and carries out down-sampling operation; Step 2, pathology section and normal tissue sections is extracted from the goldstandard image of the CT image of described liver, label according to centre of slice pixel is divided into positive sample and negative sample respectively, and adopts the method for stochastic sampling to make positive and negative sample size equal; Step 3, build multi-level degree of depth convolutional neural networks, by stochastic gradient descent method training pattern, have automatic learning tumoral character and the liver normal structure feature of supervision, obtain network model, obtain the coarse segmentation bianry image of tumour and the probabilistic image of pixel classifications by sorter; Step 4, morphological erosion operation is carried out to the coarse segmentation bianry image of described tumour, acquisition figure cuts required foreground image, again the bianry image of liver done phase reducing with the coarse segmentation bianry image of tumour and carry out morphological erosion operation, obtaining the background image corresponding to liver normal structure; Step 5, build non-directed graph according to described foreground image and background image, use figure cuts the final cut zone that optimized algorithm obtains tumour.
Further, in one embodiment, in described step 2, label according to centre of slice pixel is divided into positive sample and negative sample respectively, comprise: the corresponding tumor biopsy of positive sample, the corresponding normal tissue sections of negative sample, the positive and negative sample size inputted to make training pattern is balanced, adopts the method for stochastic sampling to make positive and negative sample size equal.
Further, in one embodiment, in described step 3, the coarse segmentation bianry image of tumour and the probabilistic image of pixel classifications is obtained by sorter, comprise: by the sorter of one deck last in network, pixel each in image is classified, obtain belonging to the probable value that tumour still belongs to liver normal structure, according to the magnitude classification of the probable value of generic, obtain the coarse segmentation bianry image of described tumour and the probabilistic image of pixel classifications.
Further, in one embodiment, in described step 5, build non-directed graph according to described foreground image and background image, use figure cuts the final cut zone that optimized algorithm obtains tumour, comprising: build non-directed graph according to described foreground image and background image, max-flow/minimal cut algorithm optimization energy function is utilized to make it reach minimum, whole pixels is divided into target or background, is labeled as 1 and 0 respectively, obtain the final cut zone of tumour.
In order to achieve the above object, the embodiment of the present invention also provides a kind of liver neoplasm segmenting device based on CT image, comprise: image pre-processing module, for carrying out Gauss except making an uproar to the CT view data of liver, being translated into gray average is 0, and variance is the standardized data of 1 and carries out down-sampling operation; Sample collection module, pathology section and normal tissue sections is extracted in goldstandard image for the CT image from described liver, label according to centre of slice pixel is divided into positive sample and negative sample respectively, and adopts the method for stochastic sampling to make positive and negative sample size equal; Model training module, for building multi-level degree of depth convolutional neural networks, by stochastic gradient descent method training pattern, there are automatic learning tumoral character and the liver normal structure feature of supervision, obtain network model, obtain the coarse segmentation bianry image of tumour and the probabilistic image of pixel classifications by sorter; Etching operation module, for carrying out morphological erosion operation to the coarse segmentation bianry image of described tumour, acquisition figure cuts required foreground image, again the bianry image of liver done phase reducing with the coarse segmentation bianry image of tumour and carry out morphological erosion operation, obtaining the background image corresponding to liver normal structure; Cut zone generation module, for building non-directed graph according to described foreground image and background image, use figure cuts the final cut zone that optimized algorithm obtains tumour.
Further, in one embodiment, described sample collection module is divided into positive sample and negative sample respectively according to the label of centre of slice pixel, specifically comprise: the corresponding tumor biopsy of positive sample, the corresponding normal tissue sections of negative sample, the positive and negative sample size inputted to make training pattern is balanced, adopts the method for stochastic sampling to make positive and negative sample size equal.
Further, in one embodiment, described model training module obtains the coarse segmentation bianry image of tumour and the probabilistic image of pixel classifications by sorter, specifically comprise: by the sorter of one deck last in network, pixel each in image is classified, obtain belonging to the probable value that tumour still belongs to liver normal structure, according to the magnitude classification of the probable value of generic, obtain the coarse segmentation bianry image of described tumour and the probabilistic image of pixel classifications.
Further, in one embodiment, described cut zone generation module builds non-directed graph according to described foreground image and background image, use figure cuts the final cut zone that optimized algorithm obtains tumour, specifically comprise: build non-directed graph according to described foreground image and background image, utilize max-flow/minimal cut algorithm optimization energy function to make it reach minimum, whole pixels is divided into target or background, be labeled as 1 and 0 respectively, obtain the final cut zone of tumour.
The present invention proposes a kind of method and device of the full automatic segmentation of the liver neoplasm based on CT image, by degree of depth learning model automatic learning feature, extract the essential characteristic that data centralization is abundanter, compared with extracting feature with hand-designed, there is better separability, optimize lesion segmentation result by figure segmentation method, make final segmentation more accurately and robust; Further, whole cutting procedure, without the need to any manual intervention, can provide accurate tumor information for the Diagnosis and Treat of liver neoplasm, contributes to the success ratio improving liver neoplasm operation.
Accompanying drawing explanation
In order to be illustrated more clearly in the embodiment of the present invention or technical scheme of the prior art, be briefly described to the accompanying drawing used required in embodiment or description of the prior art below, apparently, accompanying drawing in the following describes is only some embodiments of the present invention, for those skilled in the art, under the prerequisite not paying creative work, other accompanying drawing can also be obtained according to these accompanying drawings.
Fig. 1 is the method flow diagram of the liver neoplasm dividing method based on CT image of the embodiment of the present invention;
Fig. 2 is the schematic diagram of the multi-level degree of depth convolutional neural networks of the structure of the embodiment of the present invention;
Fig. 3 is the structural representation of the liver neoplasm segmenting device based on CT image of the embodiment of the present invention.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, be clearly and completely described the technical scheme in the embodiment of the present invention, obviously, described embodiment is only the present invention's part embodiment, instead of whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art, not making the every other embodiment obtained under creative work prerequisite, belong to the scope of protection of the invention.
The invention discloses a kind of method of the full automatic segmentation of the liver neoplasm based on CT image, its main flow is: carry out denoising, standardization, down-sampling pretreatment operation to a series of original CT liver image; Positive negative sample required for training is extracted respectively to training image and test pattern and tests the sample of required prediction label; According to training data training degree of depth convolutional neural networks model, automatically extract borderline tumor and Texture eigenvalue; Binary classifier is utilized to obtain coarse segmentation region and the probabilistic image of liver neoplasm; Re-use figure segmentation method optimization coarse segmentation result and obtain final tumor region.
Fig. 1 is the method flow diagram of the liver neoplasm dividing method based on CT image of the embodiment of the present invention.As shown in Figure 1, comprising: step S101, carrying out Gauss except making an uproar to the CT view data of liver, being translated into gray average is 0, and variance is the standardized data of 1 and carries out down-sampling operation; Step S102, pathology section and normal tissue sections is extracted from the goldstandard image of the CT image of described liver, label according to centre of slice pixel is divided into positive sample and negative sample respectively, and adopts the method for stochastic sampling to make positive and negative sample size equal; Step S103, build multi-level degree of depth convolutional neural networks (as shown in Figure 2), by stochastic gradient descent method training pattern, there are automatic learning tumoral character and the liver normal structure feature of supervision, obtain network model, obtain the coarse segmentation bianry image of tumour and the probabilistic image of pixel classifications by sorter; Step S104, morphological erosion operation is carried out to the coarse segmentation bianry image of described tumour, acquisition figure cuts required foreground image, again the bianry image of liver done phase reducing with the coarse segmentation bianry image of tumour and carry out morphological erosion operation, obtaining the background image corresponding to liver normal structure; Step S105, build non-directed graph according to described foreground image and background image, use figure cuts the final cut zone that optimized algorithm obtains tumour.
In the present embodiment, in described step S101, first to do pre-service to CT view data, the first, Gauss is carried out except making an uproar to the CT view data of liver; The second, carry out standardization, being translated into gray average is 0, and variance is the data of 1; 3rd, carry out down-sampling operation.
In the present embodiment, in described step S102, label according to centre of slice pixel is divided into positive sample and negative sample respectively, comprise: the corresponding tumor biopsy of positive sample, the corresponding normal tissue sections of negative sample, in reality, negative sample quantity will considerably beyond positive sample size, and the positive and negative sample size inputted to make training pattern is balanced, therefore adopts the method for stochastic sampling to make positive and negative sample size equal.
In the present embodiment, in described step S103, the coarse segmentation bianry image of tumour and the probabilistic image of pixel classifications is obtained by sorter, comprise: by the sorter of one deck last in network, pixel each in image is classified, obtain belonging to the probable value that tumour still belongs to liver normal structure, according to the magnitude classification of the probable value of generic, obtain the coarse segmentation bianry image of described tumour and the probabilistic image of pixel classifications.
In the present embodiment, in the degree of depth convolutional neural networks structure built, comprise convolutional layer, down-sampling layer, full articulamentum and softmax classification layer.Wherein, convolutional layer, by convolution algorithm, obtains characteristics of image, such as borderline tumor, Texture eigenvalue; Down-sampling layer carries out son sampling to the characteristic image obtained, and reduces the characteristic information that data processing amount maintains simultaneously; Full articulamentum is for flutterring the complex relationship caught between output characteristic; Softmax is a multi classifier, is that tumour and liver normal structure, its output is a conditional probability value, between 0 to 1 for two class classification here.
In network, transfer function adopts linear revise transfer function: max (0, x), if the output valve of neuron node is less than zero in network, be set to 0 by transfer function, if be greater than zero, remain unchanged, it can keep the openness of model, and what allow a multilayer neural network learn is faster.
In addition, stochastic gradient descent method is utilized to carry out minimum losses function in the present embodiment
try to achieve network parameter, namely connect corresponding weighted value θ between neuron node in network
i, stochastic gradient descent method upgrades θ by each sample
i,
In the present embodiment, in described step S105, non-directed graph is built according to described foreground image and background image, use figure cuts the final cut zone that optimized algorithm obtains tumour, comprise: build non-directed graph according to described foreground image and background image, utilize max-flow/minimal cut algorithm optimization energy function to make it reach minimum, whole pixels is divided into target or background, be labeled as 1 and 0 respectively, obtain the final cut zone of tumour.
Corresponding to said method, as shown in Figure 3, be the structural formula schematic diagram of the liver neoplasm segmenting device based on CT image of the embodiment of the present invention.The device of the present embodiment comprises: image pre-processing module 101, and for carrying out Gauss except making an uproar to the CT view data of liver, being translated into gray average is 0, and variance is the standardized data of 1 and carries out down-sampling operation; Sample collection module 102, pathology section and normal tissue sections is extracted in goldstandard image for the CT image from described liver, label according to centre of slice pixel is divided into positive sample and negative sample respectively, and adopts the method for stochastic sampling to make positive and negative sample size equal; Model training module 103, for building multi-level degree of depth convolutional neural networks, by stochastic gradient descent method training pattern, there are automatic learning tumoral character and the liver normal structure feature of supervision, obtain network model, obtain the coarse segmentation bianry image of tumour and the probabilistic image of pixel classifications by sorter; Etching operation module 104, for carrying out morphological erosion operation to the coarse segmentation bianry image of described tumour, acquisition figure cuts required foreground image, again the bianry image of liver done phase reducing with the coarse segmentation bianry image of tumour and carry out morphological erosion operation, obtaining the background image corresponding to liver normal structure; Cut zone generation module 105, for building non-directed graph according to described foreground image and background image, use figure cuts the final cut zone that optimized algorithm obtains tumour.
In the present embodiment, described sample collection module 102 is divided into positive sample and negative sample respectively according to the label of centre of slice pixel, specifically comprise: the corresponding tumor biopsy of positive sample, the corresponding normal tissue sections of negative sample, the positive and negative sample size inputted to make training pattern is balanced, adopts the method for stochastic sampling to make positive and negative sample size equal.
In the present embodiment, described model training module 103 obtains the coarse segmentation bianry image of tumour and the probabilistic image of pixel classifications by sorter, specifically comprise: by the sorter of one deck last in network, pixel each in image is classified, obtain belonging to the probable value that tumour still belongs to liver normal structure, according to the magnitude classification of the probable value of generic, obtain the coarse segmentation bianry image of described tumour and the probabilistic image of pixel classifications.
In the present embodiment, described cut zone generation module 105 builds non-directed graph according to described foreground image and background image, use figure cuts the final cut zone that optimized algorithm obtains tumour, specifically comprise: build non-directed graph according to described foreground image and background image, max-flow/minimal cut algorithm optimization energy function is utilized to make it reach minimum, whole pixels is divided into target or background, is labeled as 1 and 0 respectively, obtain the final cut zone of tumour.
The inventive method is by many cover CT3D image measurements, and experiment shows that the method has very high accuracy and robustness.
The method of the segmentation of the liver neoplasm based on CT image of the present invention and device, by degree of depth learning model automatic learning feature, extract the essential characteristic that data centralization is abundanter, compared with extracting feature with hand-designed, there is better separability, optimize lesion segmentation result by figure segmentation method, make final segmentation more accurately and robust; Further, whole cutting procedure, without the need to any manual intervention, can provide accurate tumor information for the Diagnosis and Treat of liver neoplasm, contributes to the success ratio improving liver neoplasm operation.
Those skilled in the art should understand, embodiments of the invention can be provided as method, system or computer program.Therefore, the present invention can adopt the form of complete hardware embodiment, completely software implementation or the embodiment in conjunction with software and hardware aspect.And the present invention can adopt in one or more form wherein including the upper computer program implemented of computer-usable storage medium (including but not limited to magnetic disk memory, CD-ROM, optical memory etc.) of computer usable program code.
The present invention describes with reference to according to the process flow diagram of the method for the embodiment of the present invention, equipment (system) and computer program and/or block scheme.Should understand can by the combination of the flow process in each flow process in computer program instructions realization flow figure and/or block scheme and/or square frame and process flow diagram and/or block scheme and/or square frame.These computer program instructions can being provided to the processor of multi-purpose computer, special purpose computer, Embedded Processor or other programmable data processing device to produce a machine, making the instruction performed by the processor of computing machine or other programmable data processing device produce device for realizing the function of specifying in process flow diagram flow process or multiple flow process and/or block scheme square frame or multiple square frame.
These computer program instructions also can be stored in can in the computer-readable memory that works in a specific way of vectoring computer or other programmable data processing device, the instruction making to be stored in this computer-readable memory produces the manufacture comprising command device, and this command device realizes the function of specifying in process flow diagram flow process or multiple flow process and/or block scheme square frame or multiple square frame.
These computer program instructions also can be loaded in computing machine or other programmable data processing device, make on computing machine or other programmable devices, to perform sequence of operations step to produce computer implemented process, thus the instruction performed on computing machine or other programmable devices is provided for the step realizing the function of specifying in process flow diagram flow process or multiple flow process and/or block scheme square frame or multiple square frame.
Apply specific embodiment in the present invention to set forth principle of the present invention and embodiment, the explanation of above embodiment just understands method of the present invention and core concept thereof for helping; Meanwhile, for one of ordinary skill in the art, according to thought of the present invention, all will change in specific embodiments and applications, in sum, this description should not be construed as limitation of the present invention.
Claims (8)
1. based on a liver neoplasm dividing method for CT image, it is characterized in that, described method comprises:
Step 1, carries out Gauss except making an uproar to the CT view data of liver, and being translated into gray average is 0, and variance is the standardized data of 1 and carries out down-sampling operation;
Step 2, pathology section and normal tissue sections is extracted from the goldstandard image of the CT image of described liver, label according to centre of slice pixel is divided into positive sample and negative sample respectively, and adopts the method for stochastic sampling to make positive and negative sample size equal;
Step 3, build multi-level degree of depth convolutional neural networks, by stochastic gradient descent method training pattern, have automatic learning tumoral character and the liver normal structure feature of supervision, obtain network model, obtain the coarse segmentation bianry image of tumour and the probabilistic image of pixel classifications by sorter;
Step 4, morphological erosion operation is carried out to the coarse segmentation bianry image of described tumour, acquisition figure cuts required foreground image, again the bianry image of liver done phase reducing with the coarse segmentation bianry image of tumour and carry out morphological erosion operation, obtaining the background image corresponding to liver normal structure;
Step 5, build non-directed graph according to described foreground image and background image, use figure cuts the final cut zone that optimized algorithm obtains tumour.
2. the liver neoplasm dividing method based on CT image according to claim 1, is characterized in that, in described step 2, the label according to centre of slice pixel is divided into positive sample and negative sample respectively, comprising:
The corresponding tumor biopsy of positive sample, the corresponding normal tissue sections of negative sample, the positive and negative sample size inputted to make training pattern is balanced, adopts the method for stochastic sampling to make positive and negative sample size equal.
3. the liver neoplasm dividing method based on CT image according to claim 1, is characterized in that, in described step 3, obtains the coarse segmentation bianry image of tumour and the probabilistic image of pixel classifications, comprising by sorter:
By the sorter of one deck last in network, pixel each in image is classified, obtain belonging to the probable value that tumour still belongs to liver normal structure, according to the magnitude classification of the probable value of generic, obtain the coarse segmentation bianry image of described tumour and the probabilistic image of pixel classifications.
4. the liver neoplasm dividing method based on CT image according to claim 1, is characterized in that, in described step 5, build non-directed graph according to described foreground image and background image, use figure cuts the final cut zone that optimized algorithm obtains tumour, comprising:
Build non-directed graph according to described foreground image and background image, utilize max-flow/minimal cut algorithm optimization energy function to make it reach minimum, whole pixels is divided into target or background, be labeled as 1 and 0 respectively, obtain the final cut zone of tumour.
5. based on a liver neoplasm segmenting device for CT image, it is characterized in that, described device comprises:
Image pre-processing module, for carrying out Gauss except making an uproar to the CT view data of liver, being translated into gray average is 0, and variance is the standardized data of 1 and carries out down-sampling operation;
Sample collection module, pathology section and normal tissue sections is extracted in goldstandard image for the CT image from described liver, label according to centre of slice pixel is divided into positive sample and negative sample respectively, and adopts the method for stochastic sampling to make positive and negative sample size equal;
Model training module, for building multi-level degree of depth convolutional neural networks, by stochastic gradient descent method training pattern, there are automatic learning tumoral character and the liver normal structure feature of supervision, obtain network model, obtain the coarse segmentation bianry image of tumour and the probabilistic image of pixel classifications by sorter;
Etching operation module, for carrying out morphological erosion operation to the coarse segmentation bianry image of described tumour, acquisition figure cuts required foreground image, again the bianry image of liver done phase reducing with the coarse segmentation bianry image of tumour and carry out morphological erosion operation, obtaining the background image corresponding to liver normal structure;
Cut zone generation module, for building non-directed graph according to described foreground image and background image, use figure cuts the final cut zone that optimized algorithm obtains tumour.
6. the liver neoplasm segmenting device based on CT image according to claim 5, is characterized in that, described sample collection module is divided into positive sample and negative sample respectively according to the label of centre of slice pixel, specifically comprises:
The corresponding tumor biopsy of positive sample, the corresponding normal tissue sections of negative sample, the positive and negative sample size inputted to make training pattern is balanced, adopts the method for stochastic sampling to make positive and negative sample size equal.
7. the liver neoplasm segmenting device based on CT image according to claim 5, is characterized in that, described model training module obtains the coarse segmentation bianry image of tumour and the probabilistic image of pixel classifications by sorter, specifically comprises:
By the sorter of one deck last in network, pixel each in image is classified, obtain belonging to the probable value that tumour still belongs to liver normal structure, according to the magnitude classification of the probable value of generic, obtain the coarse segmentation bianry image of described tumour and the probabilistic image of pixel classifications.
8. the liver neoplasm segmenting device based on CT image according to claim 5, it is characterized in that, described cut zone generation module builds non-directed graph according to described foreground image and background image, and use figure cuts the final cut zone that optimized algorithm obtains tumour, specifically comprises:
Build non-directed graph according to described foreground image and background image, utilize max-flow/minimal cut algorithm optimization energy function to make it reach minimum, whole pixels is divided into target or background, be labeled as 1 and 0 respectively, obtain the final cut zone of tumour.
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