Abstract: In the endoscopic examination support apparatus, the video acquisition means acquires an endoscopic video captured by an endoscope. The lesion detection means detects a lesion candidate from the endoscopic video. The heat map generation means generates a heat map which represents a lesion possibility of the lesion candidate included in the endoscopic video by a color. The display control means superimposes and displays an area where the lesion possibility is equal to or higher than a first threshold on the endoscopic video in a first display mode, and superimposes and displays an area where the lesion possibility is equal to or higher than a second threshold on the endoscopic video in a second display mode.

Abstract: In the endoscopic examination support apparatus, the image acquisition means acquires an endoscopic video captured by an endoscope. The lesion detection means detects a lesion candidate from the endoscopic video. The heat map generation means generates a heat map indicating a possibility of lesion of the lesion candidate in the endoscopic video by a color. The display control means displays a position of a lesion on a frame of the endoscopic video based on the lesion candidate and the heat map.

Abstract: In the endoscopic examination support apparatus, the image acquisition means acquires an endoscopic video captured by an endoscope. The lesion detection means detects a lesion candidate from the endoscopic video. The heat map generation means generates a heat map indicating a possibility of lesion of the lesion candidate in the endoscopic video by a color. The display control means displays a position of a lesion on a frame of the endoscopic video based on the lesion candidate and the heat map.

Abstract: In the endoscopic examination support apparatus, the image acquisition means acquires an endoscopic image taken by an endoscope. The first lesion detection means detects a lesion candidate from the endoscopic image, using a machine learning model that learned a relationship between the lesion candidate and a normal state of a large intestine. The second lesion detection means detects a lesion candidate from the endoscopic image, using a machine learning model that learned a relationship between the lesion candidate and a predetermined state of the large intestine. The output means outputs at least one of a detection result of the first lesion detection means and a detection result of the second lesion detection means.

Abstract: In the endoscopic examination support apparatus, the video acquisition means acquires an endoscopic video captured by an endoscope. The lesion detection means detects a lesion candidate from the endoscopic video. The heat map generation means generates a heat map which represents a lesion possibility of the lesion candidate included in the endoscopic video by a color. The display control means superimposes and displays an area where the lesion possibility is equal to or higher than a first threshold on the endoscopic video in a first display mode, and superimposes and displays an area where the lesion possibility is equal to or higher than a second threshold on the endoscopic video in a second display mode.

Abstract: According to one embodiment, a medical image processing apparatus includes first specifier, second specifier, determiner and display controller. First specifier collates an ischemic region calculated from a blood vessel visualized into a three-dimensional image in a plurality of phases with a dominating region of the blood vessel, and specifies a culprit vessel in the ischemic region. Second specifier specifies a culprit stenosis in the culprit vessel based on a pressure index calculated from the blood vessel. Determiner determines a connection position to connect a bypass vessel that makes a detour around the culprit stenosis. Display controller displays the determined connection position on a display.

Abstract: According to one embodiment, a medical image processing apparatus includes first specifier, second specifier, determiner and display controller. First specifier collates an ischemic region calculated from a blood vessel visualized into a three-dimensional image in a plurality of phases with a dominating region of the blood vessel, and specifies a culprit vessel in the ischemic region. Second specifier specifies a culprit stenosis in the culprit vessel based on a pressure index calculated from the blood vessel. Determiner determines a connection position to connect a bypass vessel that makes a detour around the culprit stenosis. Display controller displays the determined connection position on a display.

Abstract: According to one embodiment, a medical image diagnostic apparatus includes a storage memory, processing circuitry, and a display. The storage memory stores data of a plurality of FFR distribution maps constituting a time series regarding a coronary artery, and data of a plurality of morphological images corresponding to the time series. The processing circuitry converts the plurality of FFR distribution maps into a plurality of corresponding color maps, respectively. The display displays a plurality of superposed images obtained by superposing the plurality of color maps and the plurality of morphological images respectively corresponding in phase to the plurality of color maps. The display restricts display targets for the plurality of color maps based on the plurality of FFR distribution maps or the plurality of morphological images.

Abstract: In the endoscopic examination support apparatus, the image acquisition means acquires an endoscopic image taken by an endoscope. The first lesion detection means detects a lesion candidate from the endoscopic image, using a machine learning model that learned a relationship between the lesion candidate and a normal state of a large intestine. The second lesion detection means detects a lesion candidate from the endoscopic image, using a machine learning model that learned a relationship between the lesion candidate and a predetermined state of the large intestine. The output means outputs at least one of a detection result of the first lesion detection means and a detection result of the second lesion detection means.

Abstract: In the endoscopic examination support apparatus, the image acquisition means acquires an endoscopic image taken by an endoscope. The first lesion detection means detects a lesion candidate from the endoscopic image, using a machine learning model that learned a relationship between the lesion candidate and a normal state of a large intestine. The second lesion detection means detects a lesion candidate from the endoscopic image, using a machine learning model that learned a relationship between the lesion candidate and a predetermined state of the large intestine. The output means outputs at least one of a detection result of the first lesion detection means and a detection result of the second lesion detection means.

Abstract: In the endoscopic examination support apparatus, the video acquisition means acquires an endoscopic video captured by an endoscope. The lesion detection means detects a lesion candidate from the endoscopic video. The heat map generation means generates a heat map which represents a lesion possibility of the lesion candidate included in the endoscopic video by a color. The display control means superimposes and displays an area where the lesion possibility is equal to or higher than a first threshold on the endoscopic video in a first display mode, and superimposes and displays an area where the lesion possibility is equal to or higher than a second threshold on the endoscopic video in a second display mode.

Abstract: In the endoscopic examination support apparatus, the image acquisition means acquires an endoscopic video captured by an endoscope. The lesion detection means detects a lesion candidate from the endoscopic video. The heat map generation means generates a heat map indicating a possibility of lesion of the lesion candidate in the endoscopic video by a color. The display control means displays a position of a lesion on a frame of the endoscopic video based on the lesion candidate and the heat map.

Abstract: There is provided a medical image processing apparatus which includes a first extraction unit configured to extract coronary arteries depicted in images of a plurality of time phases relating to the heart, and to extract at least one stenosed part depicted in each coronary artery; a calculation unit configured to calculate a pressure gradient of each of the extracted coronary arteries, based on tissue blood flow volumes of the coronary arteries; a second extraction unit configured to extract an ischemic region depicted in the images; and a specifying unit configured to specify a responsible blood vessel of the ischemic region by referring to a dominance map, in which each of the extracted coronary arteries and a dominance territory are associated, for the extracted ischemic region, and to specify a responsible stenosis, based on the pressure gradient corresponding to a stenosed part in the specified responsible blood vessel.

Abstract: According to one embodiment, a medical image diagnostic apparatus includes a storage memory, processing circuitry, and a display. The storage memory stores data of a plurality of FFR distribution maps constituting a time series regarding a coronary artery, and data of a plurality of morphological images corresponding to the time series. The processing circuitry converts the plurality of FFR distribution maps into a plurality of corresponding color maps, respectively. The display displays a plurality of superposed images obtained by superposing the plurality of color maps and the plurality of morphological images respectively corresponding in phase to the plurality of color maps. The display restricts display targets for the plurality of color maps based on the plurality of FFR distribution maps or the plurality of morphological images.

Abstract: There is provided a medical image processing apparatus which includes a first extraction unit configured to extract coronary arteries depicted in images of a plurality of time phases relating to the heart, and to extract at least one stenosed part depicted in each coronary artery; a calculation unit configured to calculate a pressure gradient of each of the extracted coronary arteries, based on tissue blood flow volumes of the coronary arteries; a second extraction unit configured to extract an ischemic region depicted in the images; and a specifying unit configured to specify a responsible blood vessel of the ischemic region by referring to a dominance map, in which each of the extracted coronary arteries and a dominance territory are associated, for the extracted ischemic region, and to specify a responsible stenosis, based on the pressure gradient corresponding to a stenosed part in the specified responsible blood vessel.

Abstract: According to one embodiment, a medical image processing apparatus includes first specifier, second specifier, determiner and display controller. First specifier collates an ischemic region calculated from a blood vessel visualized into a three-dimensional image in a plurality of phases with a dominating region of the blood vessel, and specifies a culprit vessel in the ischemic region. Second specifier specifies a culprit stenosis in the culprit vessel based on a pressure index calculated from the blood vessel. Determiner determines a connection position to connect a bypass vessel that makes a detour around the culprit stenosis. Display controller displays the determined connection position on a display.

Abstract: According to one embodiment, a medical image diagnostic apparatus includes a storage memory, processing circuitry, and a display. The storage memory stores data of a plurality of FFR distribution maps constituting a time series regarding a coronary artery, and data of a plurality of morphological images corresponding to the time series. The processing circuitry converts the plurality of FFR distribution maps into a plurality of corresponding color maps, respectively. The display displays a plurality of superposed images obtained by superposing the plurality of color maps and the plurality of morphological images respectively corresponding in phase to the plurality of color maps. The display restricts display targets for the plurality of color maps based on the plurality of FFR distribution maps or the plurality of morphological images.

Abstract: According to one embodiment, a medical image processing apparatus includes first specifier, second specifier, determiner and display controller. First specifier collates an ischemic region calculated from a blood vessel visualized into a three-dimensional image in a plurality of phases with a dominating region of the blood vessel, and specifies a culprit vessel in the ischemic region. Second specifier specifies a culprit stenosis in the culprit vessel based on a pressure index calculated from the blood vessel. Determiner determines a connection position to connect a bypass vessel that makes a detour around the culprit stenosis. Display controller displays the determined connection position on a display.

Abstract: A medical image diagnosis apparatus according to an embodiment includes an imager, a storage, and processing circuitry. The storage stores therein a correspondence relationship between each of disease patterns and scans used for performing a differential diagnosis process on the disease pattern. The processing circuitry obtains, based on image data acquired by a first scan, a disease pattern having a possibility of being applicable to the subject and a first index value indicating a degree of applicability of the disease pattern. The processing circuitry outputs information indicating the disease pattern to a display when it is possible to perform the differential diagnosis process based on the first index value and outputs an imaging condition used for performing a second scan based on the correspondence relationship and the disease pattern having the possibility when it is not possible to perform the differential diagnosis process based on the first index value.

Abstract: A medical image processing apparatus according to an embodiment includes a processing circuit. The processing circuit specifies teaching data used for generation of a learned model. The processing circuit performs image analysis with respect to pieces of collected medical image data. The processing circuit extracts medical image data having an attribute common to the teaching data of the learned model from the pieces of medical image data based on an analysis result of the image analysis, as a candidate of the teaching data of the learned model.