Abstract: Methods and systems for evaluating bone lesions include accessing a first dataset acquired from a patient with a first imaging modality and a second dataset acquired from the patient with a second imaging modality. A segmentation is performed on the first dataset to identify a subset of the first dataset corresponding to a skeletal structure of the patient and a patient skeletal metric representing a total bone volume of the patient is automatically calculated from the subset of the first dataset. The methods and systems further include detection of at least one lesion in the second dataset, classification of the at least one lesion as a bone or non-bone lesion, automatic calculation of a bone lesion metric based on the classification, and calculation of a lesion burden as a ratio of the bone lesion metric and the patient skeletal metric.

Abstract: Methods and systems for evaluating bone lesions include accessing a first dataset acquired from a patient with a first imaging modality and a second dataset acquired from the patient with a second imaging modality. A segmentation is performed on the first dataset to identify a subset of the first dataset corresponding to a skeletal structure of the patient and a patient skeletal metric representing a total bone volume of the patient is automatically calculated from the subset of the first dataset. The methods and systems further include detection of at least one lesion in the second dataset, classification of the at least one lesion as a bone or non-bone lesion, automatic calculation of a bone lesion metric based on the classification, and calculation of a lesion burden as a ratio of the bone lesion metric and the patient skeletal metric.

Abstract: Methods and systems for evaluating bone lesions include accessing a first dataset acquired from a patient with a first imaging modality and a second dataset acquired from the patient with a second imaging modality. A segmentation is performed on the first dataset to identify a subset of the first dataset corresponding to a skeletal structure of the patient and a patient skeletal metric representing a total bone volume of the patient is automatically calculated from the subset of the first dataset. The methods and systems further include detection of at least one lesion in the second dataset, classification of the at least one lesion as a bone or non-bone lesion, automatic calculation of a bone lesion metric based on the classification, and calculation of a lesion burden as a ratio of the bone lesion metric and the patient skeletal metric.

Abstract: Methods and systems for evaluating bone lesions include accessing a first dataset acquired from a patient with a first imaging modality and a second dataset acquired from the patient with a second imaging modality. A segmentation is performed on the first dataset to identify a subset of the first dataset corresponding to a skeletal structure of the patient and a patient skeletal metric representing a total bone volume of the patient is automatically calculated from the subset of the first dataset. The methods and systems further include detection of at least one lesion in the second dataset, classification of the at least one lesion as a bone or non-bone lesion, automatic calculation of a bone lesion metric based on the classification, and calculation of a lesion burden as a ratio of the bone lesion metric and the patient skeletal metric.

Abstract: A method for automatically displaying an organ of interest includes accessing a series of medical images acquired from a first imaging modality, receiving an input that indicates an organ of interest, automatically detecting the organ of interest within at least one image in the series of medical images, automatically placing a visual indicator in the region of interest, and automatically propagating the visual indicator to at least one image that is acquired using a second different imaging modality. A medical imaging system and a non-transitory computer readable medium are also described herein.

Abstract: Methods and apparatus for automatically registering lesions between examinations are provided. The methods include performing a rigid registration using computed tomography (CT) images from a first image set and a second image set and performing a local non-rigid registration using positron emission tomography (PET) images from the first image set and the second image set. The methods further include automatically locating lesions on the second image set from the first image set.

Abstract: A method for automatically displaying an organ of interest includes accessing a series of medical images acquired from a first imaging modality, receiving an input that indicates an organ of interest, automatically detecting the organ of interest within at least one image in the series of medical images, automatically placing a visual indicator in the region of interest, and automatically propagating the visual indicator to at least one image that is acquired using a second different imaging modality. A medical imaging system and a non-transitory computer readable medium are also described herein.

Abstract: A method and system is provided for automatically processing a volumetric diagnostic image dataset. A first seed point is defined within a lesion. The lesion is within a first image dataset representative of a subject. A first boundary is defined in three dimensions within the first image dataset and the first seed point and the lesion are within the first boundary. At least one parameter is determined based on the first image dataset. A first segmentation algorithm is selected from a plurality of segmentation algorithms based on the at least one parameter, and the lesion is segmented using the first segmentation algorithm.

Abstract: Methods and apparatus for automatically registering lesions between examinations are provided. The methods include performing a rigid registration using computed tomography (CT) images from a first image set and a second image set and performing a local non-rigid registration using positron emission tomography (PET) images from the first image set and the second image set. The methods further include automatically locating lesions on the second image set from the first image set.

Abstract: A method is provided for managing medical imaging data. The method includes storing location information that indicates a location of an imaging data set created for a medical purpose of a patient in an electronic file created for the medical purpose.

Abstract: A method and system is provided for automatically processing a volumetric diagnostic image dataset. A first seed point is defined within a lesion. The lesion is within a first image dataset representative of a subject. A first boundary is defined in three dimensions within the first image dataset and the first seed point and the lesion are within the first boundary. At least one parameter is determined based on the first image dataset. A first segmentation algorithm is selected from a plurality of segmentation algorithms based on the at least one parameter, and the lesion is segmented using the first segmentation algorithm.