Computer tomography image analysis device and analysis method using the same

Abstract

To provide a computer tomography image analysis device capable of simply and accurately predicting the presence or absence of invasiveness of a lesion based on the proportion of the area in which the Hounsfield value exceeds a reference value out of the total volume of the lesion in a computer tomography image, and an analysis method using the same. [Solution] A computed tomography image analysis device and an analysis method using the same are provided, which include a parameter calculation unit that calculates a reference value for the Hounsfield value based on a histogram showing the distribution of the volume of the lesion in the computed tomography image and the Hounsfield value, an excess rate calculation unit that calculates an excess rate corresponding to the proportion of the area where the Hounsfield value exceeds the reference value out of the total volume of the lesion in the computed tomography image, and a prediction value calculation unit that calculates a prediction value regarding the presence or absence of invasiveness of the lesion based on whether the excess rate exceeds a cutoff value on a standard ROC curve for the reference value.

Description

[Technical field]

[0001] The present invention relates to a computed tomography image analyzing apparatus and an analysis method using the same, and more particularly to a computed tomography image analyzing apparatus capable of simply and accurately predicting the presence or absence of invasiveness of a lesion based on the proportion of an area in which the Hounsfield value exceeds a reference value to the total volume of the lesion in a computed tomography image, and an analysis method using the same. [Background technology]

[0002] Recently, with the widespread use of computed tomography (CT) scans for the early detection of lung cancer, the proportion of lesions appearing as ground-glass opacities (GGOs) being detected through medical imaging tests such as chest tomography is continually increasing.

[0003] Ground-glass nodules refer to ground-glass opacities that appear as round nodules, and are divided into part-solid ground-glass nodules (which contain solid components) and pure ground-glass nodules (pGGNs), depending on whether or not there is a solid component inside.

[0004] Homogeneous ground-glass nodules are classified as non-invasive adenocarcinoma and invasive adenocarcinoma, and the treatment methods vary depending on whether the tumor is invasive or not.

[0005] Since homogeneous ground-glass nodules do not show solid components in the image window settings for observing the lungs and mediastinum, the Hounsfield (HU) value corresponding to the brightness value of the computed tomography image of the lesion shows a relatively uniform characteristic. Therefore, there is a problem that it is difficult to predict the presence or absence of invasiveness of a lesion only by the simple Hounsfield (HU) value of the computed tomography image.

[0006] Recently, it has been confirmed that the size, volume, and shape of ground-glass nodules are known to be the main features of computed tomography images, but when the size of the lesion is small, the correlation between these features of the computed tomography images and the invasiveness of the lesion is low. It has also been confirmed that when the size of the ground-glass nodule is small, the correlation between the mean, variance, and maximum Hounsfield value of the lesion and the invasiveness of the lesion is low. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] Republic of Korea Patent Publication No. 10-2035381 (Publication date: October 22, 2019) Summary of the Invention [Problem to be solved by the invention]

[0008] The problem to be solved by the present invention is to provide a computed tomography image analyzer capable of simply and accurately predicting the presence or absence of invasiveness of a lesion based on the proportion of the area in which the Hounsfield value exceeds a reference value among the total volume of the lesion in a computed tomography image, and an analysis method using the same.

[0009] The problems to be solved by the present invention are not limited to the above problems, and other problems not mentioned will be clearly understood by a person having ordinary skill in the art to which the present invention pertains from the following description. [Means for solving the problem]

[0010] In order to solve the above problems, the present invention provides a computed tomography image analyzing device, comprising: a parameter calculation unit for calculating a reference value for a Hounsfield value based on a histogram showing the distribution of the volume of a lesion in a computed tomography image and the Hounsfield value; an excess rate calculation unit for calculating an excess rate corresponding to the proportion of an area in which the Hounsfield value exceeds the reference value among the total volume of a lesion in the computed tomography image; and a prediction value calculation unit for calculating a prediction value regarding the presence or absence of invasiveness of the lesion based on whether the excess rate exceeds a cutoff value on a standard ROC curve for the reference value.

[0011] The excess rate calculation unit can calculate the excess rate using the following [Equation 1].

[0012] [Number 1] TIFF2025500039000002.tif13166

[0013] γ means the excess rate, and V H means the volume of the area of ​​the lesion where the Hounsfield value is greater than the reference value, and V L means the volume of the area of ​​the lesion where the Hounsfield value is smaller than the reference value.

[0014] The parameter calculation unit can calculate, as the cutoff value, a candidate cutoff value at which a Youden function is maximized for candidates of cutoff values ​​on the reference ROC curve.

[0015] In addition, the parameter calculation unit can select a plurality of reference value candidates related to the reference value on the histogram, calculate each of the plurality of reference value candidates using the above [Equation 1] for each of the plurality of reference value candidates, generate candidate ROC curves for each of the exceedance rate candidate values, and then select, from among the respective exceedance rate candidate values, a reference value candidate that has a maximum area under the curve for the candidate ROC curve as the reference value.

[0016] The predicted value calculation unit can calculate the accuracy of the predicted value using the following [Equation 2] and [Equation 3].

[0017] [Number 2] TIFF2025500039000003.tif13166

[0018] [Number 3] TIFF2025500039000004.tif13166

[0019] P I is the accuracy of invasiveness, P nI means non-invasive accuracy, P r [H i = 1, γ] means the probability value of invasiveness when the lesion is invasive for the excess rate (γ), and P r [H i = 0, γ] denotes the probability value of non-invasiveness that corresponds to the case where the lesion is non-invasive for the excess rate (γ).

[0020] In addition, the parameter calculation unit can calculate a modeling graph modeled based on the actual presence or absence of invasiveness of the lesion and the empirical joint probability distribution of the actual exceedance rate, and calculate, on the modeling graph, a probability value of invasiveness corresponding to when the lesion is invasive and a probability value of non-invasiveness corresponding to when the lesion is non-invasive for the exceedance rate, and transmit these to the prediction value calculation unit.

[0021] Furthermore, when a new computed tomography image is input from the outside, the parameter calculation unit can update the reference value and the cutoff value based on the new computed tomography image.

[0022] In addition, the parameter calculation unit can newly update the modeling graph based on the actual pathological value for the invasiveness of the lesion confirmed from a previous computed tomography image, and newly update the invasiveness probability value and the non-invasiveness probability value based on the newly updated modeling graph.

[0023] According to another embodiment of the present invention, the present invention provides a computer tomography image analyzing method for analyzing a computer tomography image using the above-mentioned computer tomography image analyzing device, the method including: a reference value calculating step in which the parameter calculation unit calculates the reference value based on a histogram showing the volume of the lesion and the distribution of the Hounsfield value; an excess rate calculating step in which the excess rate calculating unit calculates an excess rate corresponding to a ratio of an area in which the Hounsfield value exceeds the reference value in the total volume of the lesion; and a prediction value calculating step in which the prediction value calculating unit calculates a prediction value regarding the presence or absence of invasiveness of the lesion based on whether the excess rate exceeds a cutoff value on a standard ROC curve for the reference value.

[0024] The computed tomography image analyzing method may further include a cutoff value calculating step, in which the parameter calculation unit calculates the cutoff value.

[0025] In the cutoff value calculation step, the parameter calculation unit can select, as the cutoff value, a candidate cutoff value that maximizes a Youden function from among candidates for cutoff values ​​on the reference ROC curve.

[0026] The reference value calculation step can include a histogram calculation step in which the parameter calculation unit shows a distribution of the lesion volume and the Hounsfield value as a histogram; a reference value candidate selection step in which the parameter calculation unit selects a plurality of reference value candidates related to the reference value on the histogram; a step in which the parameter calculation unit calculates each of the plurality of reference value candidates using the above [Equation 1]; a curve generation step in which the parameter calculation unit generates a candidate ROC curve for each of the exceedance rate candidate values; and a reference value selection step in which the parameter calculation unit selects, from each of the exceedance rate candidate values, a reference value candidate that provides a maximum area under the curve for the candidate ROC curve as the reference value.

[0027] In addition, the computed tomography image analysis method may further include a probability value calculation step in which the parameter calculation unit calculates a modeling graph modeled on the basis of the presence or absence of actual invasiveness of the lesion and an empirical joint probability distribution of an actual excess rate, and calculates, on the modeling graph, a probability value of invasiveness corresponding to a case where the lesion is invasive and a probability value of non-invasiveness corresponding to a case where the lesion is non-invasive, for the excess rate.

[0028] In addition, the computed tomography image analysis method may further include an accuracy calculation step in which the prediction value calculation unit calculates accuracy of the prediction value based on the invasive probability value and the non-invasive probability value transmitted from the parameter calculation unit.

[0029] The computer tomography image analyzing method may further include a learning step in which the parameter calculation unit updates the reference value and the cutoff value based on a new computer tomography image input from the outside.

[0030] In the learning step, the parameter calculation unit can newly update the modeling graph based on the actual pathological value for the invasiveness of the lesion confirmed from a previous computed tomography image, and newly update the probability value of invasiveness and the probability value of non-invasiveness based on the newly updated modeling graph. Effect of the Invention

[0031] The computed tomography image analyzing apparatus and the analysis method using the same according to the present invention have the advantage that an excess rate, which corresponds to the proportion of the area in which the Hounsfield value exceeds a reference value among the total volume of the lesion in the computed tomography image, is calculated, and the presence or absence of invasiveness of the lesion can be predicted simply and accurately based on whether the excess rate exceeds a cutoff value on a standard ROC curve for the reference value.

[0032] In addition, the computer tomography image analyzing apparatus and the analysis method using the same according to the present invention have the advantage that the accuracy of determining the presence or absence of invasiveness for a newly input computer tomography image can be further improved through a learning process using the probability value of invasiveness and the probability value of non-invasiveness updated based on the actual pathological value of the invasiveness of a lesion.

[0033] The effects of the present invention are not limited to the above effects, but include all effects that can be inferred from the configuration of the invention described in the detailed description of the present invention or the claims. [Brief description of the drawings]

[0034] [Figure 1] 1 is a block diagram showing the structure of a computer tomography image analysis device according to the present invention; [Diagram 2] FIG. 1 shows a histogram of Hounsfield values ​​for uniform ground-glass opacity nodules. [Diagram 3] FIG. 1 is a diagram showing an example of an ROC curve with sensitivity and specificity based on excess rate as axes. [Figure 4] 1 is a graph comparing the areas under the ROC curves for candidate reference values. [Diagram 5] FIG. 1 shows the value of the Youden function with sensitivity and specificity as variables for possible cutoff values ​​in the ROC curve. [Figure 6] FIG. 13 is a graph modeling the empirical joint probability distribution of the invasiveness or non-invasiveness of a lesion and the excess rate. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0035] Hereinafter, preferred embodiments of the present invention that can specifically achieve the above-mentioned object will be described with reference to the accompanying drawings. In describing the present embodiment, the same components are designated by the same names and reference numerals, and therefore, additional description will be omitted below.

[0036] Throughout the specification, when a part is said to be "connected (connected, contacted, or coupled)" to another part, this includes not only the case where the part is "directly connected" to another part, but also the case where the part is "indirectly connected" via another member therebetween. Furthermore, when a part is said to "include" a certain component, this does not mean that the part excludes other components, but that the part may further include other components, unless otherwise specified.

[0037] The terms used in this specification are merely used to describe certain embodiments and are not intended to limit the present invention. A singular expression includes a plural expression unless the context clearly dictates otherwise. In this specification, the terms "include" or "have" specify the presence of a feature, number, step, operation, component, part, or combination thereof described in the specification, and do not preclude the presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0038] The present invention can be used to predict the invasiveness or non-invasiveness of pathological lesions using features extracted from computed tomography images before surgery. The lesions referred to in the present invention include homogeneous ground-glass nodules, and images for homogeneous ground-glass nodules can be obtained from 3D computed tomography images of the breast.

[0039] A computer tomography image analyzing apparatus and an analyzing method using the same according to an embodiment of the present invention will be described below with reference to FIGS.

[0040] The computer tomography image analysis apparatus according to this embodiment may include an exceedance rate calculation unit 100, a predicted value calculation unit 200, and a parameter calculation unit 300.

[0041] The parameter calculation unit 300 calculates a reference value (θ HU ) is calculated. The parameter calculation unit 300 calculates the reference value (θ HU The process of calculating ) is as follows:

[0042] First, when a DICOM (Digital Imaging and Communication in Medicine) type file for a lesion is transmitted from the outside, the parameter calculation unit 300 can obtain the volume of the lesion and the distribution of the Hounsfield values ​​of a computed tomography (CT) image in the form of a histogram as shown in Fig. 2. That is, the parameter calculation unit 300 can display the volume of the lesion and the distribution of the Hounsfield values ​​as a histogram.

[0043] Here, DICOM means a standard used for expressing digital image data in medical equipment or communicating using digital image data.HU ) is -300HU.

[0044] Next, the parameter calculation unit 300 calculates the reference value (θ HU 2, in this embodiment, the reference value candidates can be selected from the range of Hounsfield values ​​appearing on the histogram, that is, between −1000 HU and 100 HU.

[0045] Preferably, the reference value candidates can be selected within a range where high Hounsfield values ​​in the lesion are distributed on average. For example, the parameter calculation unit 300 can select the reference value candidates based on a range where previous reference values ​​selected from previous pre-stored computed tomography images are distributed. In this embodiment, based on FIG. 2, the reference value candidates are selected from a range of -500HU to 100HU, and the interval of the reference value candidates is set to 50.

[0046] Next, the parameter calculation unit 300 calculates each exceedance rate candidate value for each reference value candidate using [Equation 1].

[0047] The parameter computation unit 300 then generates a candidate ROC curve for each candidate rate of exceedance value.

[0048] Fig. 3 is a diagram showing an example of a candidate ROC curve with axes of sensitivity and specificity (1-specificity) for the excess rate candidate value when the lesion is classified as invasive or non-invasive. Referring to Fig. 3, it can be seen that the candidate ROC curve is in the upper region of the reference line.

[0049] Next, the parameter calculation unit 300 selects, from among the exceedance rate candidate values, a reference value candidate that maximizes the area under the curve (AUC: Area Under ROC Curve) for the candidate ROC curve as a reference value.

[0050] 4 is an example of a graph comparing the area under the curve (AUC) for candidate ROC curves for candidate exceedance rates. Referring to FIG. 4, in this embodiment, −300 HU, which maximizes the area under the curve (AUC), is selected as the reference value.

[0051] On the other hand, the excess rate calculation unit 100 calculates the Hounsfield value of the total volume of the lesion in the computed tomography image of the input lesion as the reference value (θ HU ) can be calculated as the excess rate (γ).

[0052] The excess rate (γ) can be calculated by the following [Equation 1].

[0053] [Number 1] TIFF2025500039000005.tif13166

[0054] Here, γ means the excess rate, and V H The Hounsfield value of the lesion is the standard value (θ HU ), and V L The Hounsfield value of the lesion is the standard value (θ HU ) means the volume of a region smaller than

[0055] Also, V H and V L is obtained from a graph showing the distribution of the volume and House field value for the lesion in the form of a histogram. For example, in the histogram shown in FIG. 2, the reference value (θ HU ) as a standard, V H is the Hounsfield value of the standard value (θ HU ) and corresponds to the area under the graph in the area larger than V L is the Hounsfield value of the standard value (θ HU ) is the area under the graph.

[0056] The excess rate calculation unit 100 calculates the reference value (θ HU ) is transmitted from the parameter calculation unit 300, and the excess rate (γ) can be calculated by [Equation 1].

[0057] TIFF2025500039000006.tif32166

[0058] The predicted value calculation unit 200 calculates the excess rate (γ) based on the cutoff value (θ γ ), the predicted value is output as 1, and the excess rate (γ) is the cutoff value (θ γ ), the predicted value can be output as 0.

[0059] TIFF2025500039000007.tif20166

[0060] The predicted value calculation unit 200 calculates the cutoff value (θ γ ) is transmitted from the parameter calculation unit 300.

[0061] The parameter calculation unit 300 calculates the cutoff value (θ γ The process of calculating ) is as follows:

[0062] The parameter calculation unit 300 calculates Youden function values ​​for all cutoff value candidates on the reference ROC curve for the selected reference value. The Youden function values ​​are calculated through Youden's Index, so a detailed description thereof will be omitted.

[0063] The parameter calculation unit 300 selects a candidate cutoff value that maximizes the Youden function value as the cutoff value (θ γ ) Referring to FIG. 5, in this embodiment, the cutoff value is selected to be 5.41%. On the other hand, the prediction value calculation unit 200 calculates the excess rate (γ) and the cutoff value (θ γ ) the accuracy of the predicted value corresponding to the difference between the predicted value and the actual value can be calculated.

[0064] The accuracy of the predicted value is determined by the excess rate (γ) and the cutoff value (θ γ ) or more for the accuracy of invasiveness (P I ) and the excess rate (γ) is the cutoff value (θ γ ) for noninvasive accuracy (P nI ).

[0065] Invasive Precision (P I ) is calculated by [Equation 2], and the non-invasive precision (P nI ) is calculated using [Equation 3].

[0066] [Number 2] TIFF2025500039000008.tif13166

[0067] [Number 3] TIFF2025500039000009.tif13166

[0068] Here, P r [H i = 1, γ] means the probability value of invasiveness when the lesion is invasive for the excess rate (γ), and P r [H i = 0, γ] denotes the probability value of non-invasiveness that corresponds to the case where the lesion is non-invasive for the excess rate (γ).

[0069] TIFF2025500039000010.tif20166

[0070] Invasive Precision (P I ) is the excess rate (γ) when the cutoff value (θ γ ), that is, the greater the excess rate (γ) and the cutoff value (θ γ ) the closer it is to 100, the greater the difference is. I ) increases. In addition, the non-invasive accuracy (P nI ) is the excess rate (γ) when the cutoff value (θ γ ), that is, the greater the excess rate (γ) and the cutoff value (θγ ) the greater the difference, the closer it is to 100, and the greater the accuracy of non-invasiveness (P nI ) increases.

[0071] The prediction value calculation unit 200 calculates the probability value of invasiveness (P r [H i = 1, γ]) and the probability value of non-invasiveness (P r [H i = 0, γ]) is transmitted from the parameter calculation unit 300.

[0072] The process in which the parameter calculation unit 300 calculates the probability value of invasiveness and the probability value of non-invasiveness is as follows.

[0073] Probability value of invasiveness (P r [H i = 1, γ]) and the probability value of noninvasiveness (P r [H i = 0, γ]) can be obtained from a modeling graph modeled based on the empirical joint probability distribution of the actual invasiveness or non-invasiveness of the lesion and the actual excess rate.

[0074] First, the parameter calculation unit 300 calculates a modeling graph that is modeled based on the empirical joint probability distribution of the actual presence or absence of invasiveness of a lesion and the actual excess rate.

[0075] As shown in Figure 6, the modeling graph is a graph obtained by curve fitting based on the actual values ​​for the presence or absence of invasiveness of the lesion obtained from the actual pathological results and the excess rate values ​​at that time.

[0076] The empirical joint probability distribution satisfies the following [Equation 4], [Equation 5], and [Equation 6].

[0077] [Number 4] TIFF2025500039000011.tif13166

[0078] [Number 5] TIFF2025500039000012.tif13166

[0079] [Number 6] TIFF2025500039000013.tif13166

[0080] Here, N t is the number of uniform ground-glass nodules, i.e. lesions, collected, and N I and N nI are the numbers of lesions classified as invasive adenocarcinoma and noninvasive adenocarcinoma, respectively, among the collected lesions.

[0081] In FIG. 6, G1 is a curve showing the joint probability distribution according to the excess rate when the lesion is invasive, and G0 is a curve showing the joint probability distribution according to the excess rate when the lesion is noninvasive.

[0082] The parameter calculation unit 300 then calculates, on the modeling graph, a probability value of invasiveness (P r [H i = 1, γ]) and the probability of noninvasiveness when the lesion is noninvasive (P r [H i = 0, γ]).

[0083] As shown in Figure 6, when the value of exceedance rate (γ) is α, the probability value of invasiveness (P r [H i = 1, γ]) is the value corresponding to α in the G1 graph, and the probability value of non-invasiveness (P r [H i = 0, γ]) is calculated as the value corresponding to α in the G0 graph. Here, the probability value of invasiveness (P r [H i = 1, γ]) and the probability value of noninvasiveness (P r [H i =0, γ]) can have a value between 0 and 1.

[0084] The parameter calculation unit 300 calculates the calculated probability value of invasiveness (P r [Hi = 1, γ]) and the probability value of non-invasiveness (P r [H i = 0, γ]) to the prediction calculation unit 200.

[0085] As a result, the value of the exceedance rate (γ) is increased by the cutoff value (θ γ ) is greater than the invasive precision (P I ) is output as a value close to 100, and the excess rate (γ) is the cutoff value (θ γ ), the accuracy of invasiveness (P I ) is output as a value of about 50. In this way, the accuracy of the predicted value is output as a percentile, thereby improving the reliability of the predicted value for the invasiveness of the lesion.

[0086] Meanwhile, when a new computed tomography image is input from the outside, the parameter calculation unit 300 can update the reference value and the cutoff value based on the new computed tomography image.

[0087] The parameter calculation unit 300 transmits the newly updated reference value to the exceedance rate calculation unit 100 to which a new computed tomography image has been input, and also transmits the newly updated cutoff value to the prediction value calculation unit 200.

[0088] In addition, the parameter calculation unit 300 can newly update the modeling graph based on the pathological actual value for the invasiveness of the lesion confirmed in the previous step. For example, the G1 curve and the G0 curve in Fig. 6 can be newly set by adding the pathological result for the presence or absence of invasiveness of the new lesion.

[0089] The parameter calculation unit 300 can newly update the probability value of invasiveness and the probability value of non-invasiveness based on the newly updated modeling graph and transmit them to the prediction value calculation unit 200 .

[0090] The process of analyzing a computer tomography image using the above-mentioned computer tomography image analyzing apparatus will be described as follows.

[0091] First, the parameter calculation unit 300 performs a reference value calculation step of calculating a reference value based on a histogram showing the distribution of the volume and Hounsfield value of a lesion in a computed tomography image.

[0092] When a computed tomography image of a lesion is input to the parameter calculation unit 300, the parameter calculation unit 300 calculates a reference value (θ HU ) and transmits the calculated reference value to the exceedance rate calculation unit 100.

[0093] In the reference value calculation step, the parameter calculation unit 300 first performs a histogram calculation step in which the distribution of the lesion volume and the Hounsfield value is displayed as a histogram.

[0094] Next, the parameter calculation unit 300 performs a reference value candidate selection step of selecting a plurality of reference value candidates related to the reference value on the histogram.

[0095] Next, the parameter calculation unit 300 calculates each exceedance rate candidate value for each of the plurality of reference value candidates using the above [Equation 1].

[0096] The parameter computation unit 300 then performs a curve generation step to generate candidate ROC curves for each of the candidate exceedance rates.

[0097] Next, the parameter calculation unit 300 performs a reference value selection step of selecting, as the reference value, a reference value candidate that maximizes the area under the curve with respect to the candidate ROC curve from among the exceedance rate candidate values.

[0098] In addition, the parameter calculation unit 300 calculates the reference value (θ HU) based on the reference ROC curve, the cutoff value (θ γ ) and transmits the calculated cutoff value to the predicted value calculation unit 200. That is, a cutoff value calculation step is performed in which the parameter calculation unit 300 calculates the cutoff value.

[0099] In the cutoff value calculation step, a cutoff value candidate that maximizes a Youden function is selected as the cutoff value from among the cutoff value candidates on the reference ROC curve.

[0100] After the calculation process for the reference value and the cutoff value is completed, the excess rate calculation unit 100 calculates the excess rate (γ), which corresponds to the proportion of the area in which the Hounsfield value exceeds the reference value among the total volume of the lesion in the computed tomography image of the lesion, using [Equation 1].

[0101] Next, the prediction value calculation unit 200 calculates the cutoff value (θ) on the reference ROC curve for the reference value. γ A predicted value calculation step is performed to calculate a predicted value regarding the presence or absence of invasiveness of the lesion based on whether the predicted value exceeds the threshold value.

[0102] As a result, the present invention has the advantage of being able to simply and accurately predict the presence or absence of invasiveness of ground-glass opacity nodules with little solid components based on a distribution map of Hounsfield values ​​obtained by image analysis of three-dimensional chest computed tomography images.

[0103] Next, the parameter calculation unit 300 performs a probability value calculation step to calculate a probability value of invasiveness and a probability value of non-invasiveness, which are used to calculate the accuracy of the prediction value.

[0104] In the probability value calculation step, the parameter calculation unit 300 first performs a graph calculation step of calculating a modeling graph modeled based on the empirical joint probability distribution of the actual presence or absence of invasiveness to a lesion and the actual excess rate.

[0105] Next, the parameter calculation unit 300 performs a probability value calculation step of calculating, on the modeling graph, a probability value of invasiveness when the lesion is invasive and a probability value of non-invasiveness when the lesion is non-invasive, relative to the exceedance rate.

[0106] The parameter calculation unit 300 then transmits the invasive probability value and the non-invasive probability value to the prediction value calculation unit 200 .

[0107] Thereafter, the prediction value calculation unit 200 performs an accuracy calculation step in which it calculates the accuracy of the prediction value based on the invasive probability value and the non-invasive probability value transmitted from the parameter calculation unit 300 using [Equation 2] and [Equation 3].

[0108] The accuracy of the prediction value may include accuracy of invasiveness when the exceedance rate is equal to or greater than the cutoff value, and accuracy of non-invasiveness when the exceedance rate is less than the cutoff value. Of course, the present invention is not limited to this, and the predicted value calculation step and the accuracy calculation step may be performed simultaneously.

[0109] Meanwhile, the CT image analyzing method may further include a learning step in which the parameter calculation unit 300 updates the reference value and the cutoff value based on a new CT image input from the outside.

[0110] In the learning step, the parameter calculation unit can newly update the modeling graph based on the actual pathological value for the invasiveness of the lesion confirmed from a previous computed tomography image, and newly update the probability value of invasiveness and the probability value of non-invasiveness based on the newly updated modeling graph.

[0111] As a result, through a learning process using updated probability values ​​of invasiveness and non-invasiveness based on actual pathological values ​​for the invasiveness of the lesion identified from previous CT scans, the accuracy of determining the presence or absence of invasiveness for newly input CT scans can be further improved.

[0112] As described above, the present invention is not limited to the specific preferred embodiments described above, and various modifications can be made by a person having ordinary skill in the art to which the invention pertains without departing from the gist of the present invention as claimed in the claims, and such modifications fall within the scope of the present invention. [Explanation of symbols]

[0113] 100: Excess rate calculation unit 200: Prediction value calculation unit 300: Parameter calculation unit

Claims

1. a parameter calculation unit for calculating a reference value for the Hounsfield value based on a histogram showing the distribution of the volume of the lesion and the Hounsfield value in the computed tomography film image; an excess rate calculation unit for calculating an excess rate corresponding to a ratio of an area in which the Hounsfield value exceeds the reference value to a total volume of the lesion in the computed tomography image; and a prediction value calculation unit for calculating a prediction value regarding the presence or absence of invasiveness of the lesion based on whether or not the excess rate exceeds a cutoff value on a reference ROC curve for the reference value; The exceedance rate calculation unit is represented by the following [Equation 1]: [Equation 1] (γ means excess rate, V Ｈ means the volume of the area of ​​the lesion where the Hounsfield value is greater than the reference value, and V Ｌ means the volume of the area of ​​the lesion where the Hounsfield value is smaller than the reference value) Calculate the excess rate using A computer tomography image analysis apparatus comprising:

2. The parameter calculation unit calculates a candidate cutoff value at which a Youden function is maximized for the candidates of the cutoff value on the reference ROC curve as the cutoff value.

2. A computer tomography image analysis apparatus as claimed in claim 1.

3. the parameter calculation unit selects a plurality of reference value candidates related to the reference value on the histogram; Calculating each excess rate candidate value for each of the plurality of reference value candidates using the above [Equation 1]; After generating candidate ROC curves for each of the exceedance rate candidate values, a reference value candidate that has a maximum area under the candidate ROC curve is selected as the reference value from among the exceedance rate candidate values.

3. A computer tomography image analysis apparatus as claimed in claim 2.

4. The prediction value calculation unit is based on the following [Equation 2] and [Equation 3]: [Equation 2] [Equation 3] (P Ｉ is the accuracy of invasiveness, P ｎＩ means non-invasive accuracy, P ｒ [H ｉ = 1, γ] means the probability value of invasiveness that corresponds to the case where the lesion is invasive for the excess rate (γ), and P ｒ [H ｉ = 0, γ] means the probability value of non-invasiveness that corresponds to the case where the lesion is non-invasive for the excess rate (γ)) Calculate the accuracy of the predicted value using 2. A computer tomography image analysis apparatus as claimed in claim 1.

5. The parameter calculation unit calculates a modeling graph based on an empirical joint probability distribution of the actual invasiveness or non-invasiveness of the lesion and the actual excess rate; On the modeling graph, a probability value of invasiveness corresponding to the case where the lesion is invasive and a probability value of non-invasiveness corresponding to the case where the lesion is non-invasive are calculated for the excess rate, and transmitted to the prediction value calculation unit.

5. Apparatus according to claim 4, wherein said apparatus is adapted for analysing computed tomography images.

6. When a new computed tomography image is input from the outside, the parameter calculation unit updates the reference value and the cutoff value based on the new computed tomography image.

4. Apparatus according to claim 3, wherein said apparatus comprises:

7. The parameter calculation unit updates the modeling graph based on the pathological actual value for the invasiveness of the lesion identified from the previous computed tomography image; The probability value of invasiveness and the probability value of non-invasiveness are updated based on the newly updated modeling graph.

6. A computer tomography image analysis apparatus as claimed in claim 5.

8. 2. A method for analyzing a computed tomography image using the computed tomography image analysis apparatus of claim 1, comprising: a reference value calculation step in which the parameter calculation unit calculates the reference value based on a histogram indicating a distribution of the volume of the lesion and the Hounsfield value; an excess rate calculation step in which the excess rate calculation unit calculates an excess rate corresponding to a ratio of an area in which the Hounsfield value exceeds the reference value in the total volume of the lesion; A prediction value calculation step in which the prediction value calculation unit calculates a prediction value regarding the presence or absence of invasiveness of the lesion based on whether or not the excess rate exceeds a cutoff value on a standard ROC curve for the reference value.

13. A method for analyzing computed tomography images comprising:

9. The parameter calculation unit further includes a cutoff value calculation step of calculating the cutoff value, In the cutoff value calculation step, the parameter calculation unit selects, as the cutoff value, a candidate cutoff value for which a Youden function is maximized from among the candidates for cutoff values ​​on the reference ROC curve.

9. A method for analyzing computed tomography images according to claim 8.

10. The reference value calculation step includes: a histogram calculation step in which the parameter calculation unit displays a distribution of the volume of the lesion and the Hounsfield value as a histogram; a reference value candidate selection step in which the parameter calculation unit selects a plurality of reference value candidates related to the reference value on the histogram; the parameter calculation unit calculating each exceedance rate candidate value for each of the plurality of reference value candidates using the above [Equation 1]; a curve generating step in which the parameter calculation unit generates a candidate ROC curve for each of the exceedance rate candidate values; The parameter calculation unit includes a reference value selection step of selecting, as the reference value, a reference value candidate that has a maximum area under the curve for the candidate ROC curve from among the exceedance rate candidate values.

9. A method for analyzing computed tomography images according to claim 8.

11. The parameter calculation unit calculates a modeling graph based on an empirical joint probability distribution of the actual invasiveness or non-invasiveness of the lesion and the actual excess rate; The method further includes a probability value calculation step of calculating a probability value of invasiveness corresponding to the case where the lesion is invasive and a probability value of non-invasiveness corresponding to the case where the lesion is non-invasive, for the excess rate on the modeling graph.

9. A method for analyzing computed tomography images according to claim 8.

12. The prediction value calculation unit further includes an accuracy calculation step of calculating accuracy of the prediction value based on the invasive probability value and the non-invasive probability value transmitted from the parameter calculation unit.

12. The method of claim 11 .

13. The parameter calculation unit further includes a learning step of updating the reference value and the cutoff value based on a new computed tomography image input from the outside.

12. The method of claim 11 .

14. In the learning step, the parameter calculation unit newly updates the modeling graph based on the pathological actual value for the invasiveness of the lesion confirmed from the previous computed tomography movie image, and newly updates the probability value of invasiveness and the probability value of non-invasiveness based on the newly updated modeling graph.

14. The method of claim 13.

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