Patent Description:
Medical imaging has emerged as a primary tool for diagnosis of several diseases. Medical imaging is the process of creating visual representations of an interior of a body for clinical analysis and medical intervention, as well as for functional analysis of internal organs or tissues in the body. Generally, the visual representations may be in the form of images and videos. One of the first and most important process used for analyzing the visual representations is segmentation. Segmentation is the process of partitioning an image into different meaningful segments, which correspond to different tissue classes, organs, pathologies, or other biologically relevant structures.

In general, the human anatomy consists of many types of tissues. Consequently, in certain diagnostic processes, particularly in the diagnosis of tumors, a single tissue may appear differently in different modalities. Hence, using a common segmentation process, such as a computer-based automatic tumor segmentation has remained an ongoing challenge in the diagnosis of tumors.

Also, most of the existing segmentation procedures are tailored for segmenting tumors in the images of specific modalities and for a particular anatomy. Thus, presently, there still is need for segmentation techniques that are generic across modalities and anatomies.

The information disclosed in this background of the disclosure section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.

Document <NPL>, discloses a weakly supervised part segmentation method which segments part regions from multiple images based on several seeds on an image using seed propagation among multiple images and part generation from seeds. Based on the seeds given by a user, the seeds and object regions for all images are obtained by object cosegmentation and seeds matching.

Disclosed herein is a method for automatically propagating segmentation in a medical image according to claim <NUM>.

Further, the present disclosure relates to an image segmentation system for automatically propagating segmentation in a medical image according to claim <NUM>.

The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, explain the disclosed principles. The same numbers are used throughout the figures to reference like features and components. Some embodiments of system and/or methods in accordance with embodiments of the present subject matter are now described, by way of example only, and regarding the accompanying figures, in which:.

It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative systems embodying the principles of the present subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer readable medium and executed by a computer or processor, whether such computer or processor is explicitly shown.

While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however that it is not intended to limit the disclosure to the specific forms disclosed.

The terms "comprises", "comprising", "includes", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, device, or method that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or device or method. In other words, one or more elements in a system or apparatus proceeded by "comprises. a" does not, without more constraints, preclude the existence of other elements or additional elements in the system or method.

Embodiments of the present disclosure may be used for automatically propagating segmentation of lesions through one or more follow-up sessions for oncology during periodic scans of the lesions and/or other affected regions.

Accordingly, in an embodiment, the present disclosure discloses a method and an image segmentation system for automatically propagating segmentation in a medical image. In an embodiment, the method of present disclosure comprises picking up reference points corresponding to a segmented Region of Interest (RoI) of reference image and/or reference study and translating the reference points to a current image of the lesion captured in the follow-up session to perform multi-seed segmentation. Multi-seed segmentation of the selected relevant reference points on the current image helps in estimating and segmenting a target RoI in the current image. In an embodiment, the target RoI may be a propagated segmentation of the segmented RoI in the reference image. Thus, the present disclosure helps in automatically propagating the segmentation in medical images.

In an embodiment, the present disclosure performs segmentation of lesion for any anatomical region and for any modality to infer required parameters from the segmented lesion. As an example, the parameters that may be inferred may include, without limiting to, size, shape, position, intensity distribution of the lesion, homogeneity/heterogeneity of lesion and appearance of surrounding background tissue of the lesion.

In an embodiment, the present disclosure addresses a technical problem of repetitive segmentation of lesion in the follow-up scans by automating the propagation of segmentation using the information of lesion segmented in the first scan.

In an embodiment, the method and the image segmentation system disclosed in the present disclosure may be used for propagation of the segmentation of objects/entities having similar features in more than one studies such as scans, images, volumes, when segmentation of the object/entity is available in at least one of such studies.

In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings that form a part hereof, and in which are shown by way of illustration specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.

<FIG> illustrates an exemplary environment for automatically propagating segmentation in medical images in accordance with some embodiments of the present disclosure.

The environment <NUM> may include an image segmentation system <NUM> and a reference database <NUM> associated with the image segmentation system <NUM>. In an embodiment, the image segmentation system <NUM> may be a computing device such as, without limiting to, a desktop computer, a laptop, a smartphone or a server, which may be configured for automatically propagating segmentations in medical images in accordance with the embodiments of the present disclosure. In an embodiment, the reference database <NUM> may be a storage unit used for storing information related to patients, historical medical images and/or historical reference image <NUM> related to the patients and other information required for propagating the segmentation. In an implementation, the reference database <NUM> may be a part of the image segmentation system <NUM> and present within the image segmentation system <NUM>.

In an embodiment, the reference image <NUM> and the current image <NUM> may be images of entities such as organs, tissues, bones, air cavities and muscles of a subject, which are captured at different time periods. Here, the subject may be a human being, an animal and the like. Further, as an example, the reference image <NUM> may be an image of an entity captured during an initial study of the entity, from which a reference Region of Interest (RoI) may be segmented. The current image <NUM> may be an image of the same entity captured during a subsequent study of the subject. In an embodiment, the reference image <NUM> and the current image <NUM> may be captured by an external equipment such as a Computed Tomography (CT) scanner, a Magnetic Resonance Imaging (MRI) device and the like. In an embodiment, the image segmentation system <NUM> may receive the reference image <NUM> and the current image <NUM> from one of the medical equipment or a reference database using a wired and/or a wireless communication interface configured between the image segmentation system <NUM> and the external equipment. In an implementation, the image segmentation system <NUM> and the external equipment may be implemented as a single system, which performs both scanning and segmentation.

In an embodiment, upon capturing the reference image <NUM> of the entity, a technical expert associated with the image segmentation system <NUM> may manually perform segmentation of the reference image <NUM> to mark and/or to obtain the reference RoI from the reference image <NUM>. Subsequently, upon capturing the current image <NUM> of the entity, the current image <NUM>, along with the reference image <NUM> and the segmented reference RoI, may be provided as an input to the image segmentation system <NUM> for automatically performing segmentation of the current image <NUM>.

In an embodiment, upon receiving the reference image <NUM>, the current image <NUM> and the reference RoI, the image segmentation system <NUM> may determine one or more segmentation parameters related to the reference image <NUM> based on analysis of the segmented reference RoI in the reference image <NUM>. In an embodiment, the one or more segmentation parameters determined from the reference image <NUM> include, without limiting to, one or more morphological characteristics of the segmented reference RoI, a position of the segmented reference RoI within the reference image <NUM>, an intensity distribution of the segmented reference RoI, and a background intensity distribution of a background of the segmented reference RoI.

In an embodiment, the one or more morphological characteristics include, without limiting to, a short axis and a longest diameter of the segmented reference RoI and size and shape of the segmented reference RoI. In an embodiment, the size and shape of the segmented reference RoI may be determined based on length and coordinates of the short axis and the longest diameter of the segmented reference RoI.

In an embodiment, the intensity distribution of the segmented reference RoI may be variation of pixel intensities in the region of the segmented reference RoI. Similarly, the background intensity distribution of the background of the segmented reference RoI may be the variation of pixel intensities in the region around the segmented reference RoI. In an embodiment, the intensity distribution of the background of segmented reference RoI may be determined by determining a plurality of intensity distributions of a plurality of background pixels/points corresponding to quadrants of the segmented reference RoI, with respect to a centre of the segmented reference RoI. In an embodiment, the intensity distribution ranges may be determined based on an amount of overlapping between the pixel intensities of the respective background of the plurality of background quadrants and pixel intensities of the segmented reference RoI.

In an embodiment, upon determining the one or more segmentation parameters from the segmented reference RoI, the image segmentation system <NUM> determines a plurality of reference points corresponding to the segmented reference RoI based on the one or more morphological characteristics of the segmented reference RoI. In an embodiment, the plurality of reference points may be the points located along the short axis and the longest diameter of the segmented reference RoI.

In an embodiment, subsequent to determining the plurality of reference points, the image segmentation system <NUM> generates a plurality of translated points on the current image <NUM> of the entity, in which a target RoI <NUM> has to be segmented. In an embodiment, the plurality of translated points is obtained by translating each of the plurality of reference points onto the current image <NUM>.

In an embodiment, upon generating the plurality of translated points, the image segmentation system <NUM> automatically selects relevant seeds among the plurality of translated points in the current image <NUM>, based on the one or more segmentation parameters.

In an embodiment, once the relevant seeds are selected from the plurality of translated points, the image segmentation system <NUM> performs a multi-seed segmentation of the selected relevant seeds for estimating and segmenting the target RoI <NUM> in the current image <NUM>. In an embodiment, the target RoI <NUM> corresponds to a propagated segmentation of the segmented RoI in the reference image <NUM>. In an embodiment, a prerequisite condition for performing the segmentation of the target RoI <NUM> may be that the pixel intensities of the current image <NUM> must be quantitatively comparable or normalized to the pixel intensities of the reference image <NUM>.

In an embodiment, after segmenting the target RoI <NUM> from the current image <NUM>, the target RoI <NUM> may be compared against the segmented reference RoI to determine changes in the respective one or more segmentation parameters of the segmented reference RoI and the target RoI <NUM>. In an embodiment, during subsequent studies of the entity, the current image <NUM> of the entity and the segmented target RoI <NUM> together may be considered as the reference image <NUM> having the reference segmented RoI.

<FIG> shows a detailed block diagram illustrating an image segmentation system <NUM> for automatically propagating the segmentation in accordance with some embodiments of the present disclosure.

In an implementation, the image segmentation system <NUM> may include an I/O interface <NUM>, a processor <NUM>, and a memory <NUM>. The I/O interface <NUM> may be configured to communicate with one or more sources and/or external equipment for receiving a reference image <NUM> and a current image <NUM>. Further, the I/O interface <NUM> may be used to connect the image segmentation system <NUM> to a display interface for displaying the reference image <NUM>, the current image <NUM> and segmented regions of the images to a user. In an embodiment, the memory <NUM> may be communicatively coupled to the processor <NUM>. The processor <NUM> may be configured to perform one or more functions of the image segmentation system <NUM>.

In some implementations, the image segmentation system <NUM> may include data <NUM> and modules <NUM> for performing various operations in accordance with the embodiments of the present disclosure. In an embodiment, the data <NUM> may be stored within the memory <NUM> and may include, without limiting to, one or more segmentation parameters <NUM>, a plurality of reference points <NUM>, a plurality of translated points <NUM>, a Target Region of Interest (RoI) <NUM> and other data <NUM>.

In some embodiments, the data <NUM> may be stored within the memory <NUM> in the form of various data structures. Additionally, the data <NUM> may be organized using data models, such as relational or hierarchical data models. The other data <NUM> may store temporary data and temporary files, generated by the modules <NUM> while performing various functions of the image segmentation system <NUM>. As an example, the other data <NUM> may include, without limiting to, one or more historical or reference image <NUM> of the entity, morphological characteristics of the segmented RoIs and the like.

In an embodiment, the data <NUM> may be processed by one or more modules <NUM> of the image segmentation system <NUM>. As used herein, the term module refers to an Application Specific Integrated Circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or group) and memory that execute one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that provide the described functionality. In an embodiment, the other modules <NUM> may be used to perform various miscellaneous functionalities of the image segmentation system <NUM>. It will be appreciated that such modules <NUM> may be represented as a single module or a combination of different modules.

In one implementation, the one or more modules <NUM> may be stored as instructions executable by the processor <NUM>. In another implementation, each of the one or more modules <NUM> may be separate hardware units, communicatively coupled to the processor <NUM> for performing one or more functions of the image segmentation system <NUM>. The one or more modules <NUM> may include, without limiting to, a parameter determination module <NUM>, a reference point determination module <NUM>, a seed generation module <NUM>, a seed selection module <NUM>, a segmentation module <NUM> and other modules <NUM>.

In an embodiment, the parameter determination module <NUM> may be used for determining one or more segmentation parameters <NUM> based on analysis of the segmented reference RoI in the reference image <NUM>. In an embodiment, the foremost step towards propagating segmentation of the reference image <NUM> may be determination of the one or more segmentation parameters <NUM> such as, without limiting to, one or more morphological characteristics of the segmented reference RoI, a position of the segmented reference RoI within the reference image <NUM>, an intensity distribution of the segmented reference RoI, and an intensity distribution of a background of the segmented reference RoI.

In an embodiment, the parameter determination module <NUM> may determine the one or more morphological characteristics of the segmented reference RoI by determining a short axis and a longest diameter of the segmented reference RoI and then determining a size and a shape of the segmented reference RoI based on length and coordinates of the short axis and the longest diameter of the segmented reference RoI. Further, the position of the segmented reference RoI within the reference image <NUM> may be determined based on co-ordinates of the points on the short axis and the longest diameter.

In an embodiment, the parameter determination module <NUM> may determine the intensity distribution of the segmented reference RoI by computing a histogram of intensity of the pixels forming the segmented reference RoI. In addition, the parameter determination module <NUM> may derive a heterogeneity of the segmented reference RoI by thresholding the histogram at a predetermined frequency. For example, the pixel intensity values having frequency values greater than <NUM>% of the highest frequency value in the histogram may be considered. The thresholding may result in a number of pixel intensity ranges, referred as Lesion Range (Lr). In an embodiment, if the segmented reference RoI is homogeneous, then there may be only a single pixel intensity range. On the other hand, if the segmented reference RoI is heterogeneous, then thresholding of the histogram may result in multiple pixel intensity ranges.

In an embodiment, the parameter determination module <NUM> may determine the intensity distribution of the background and/or surrounding region of the segmented reference RoI by a process that is similar to computation of the histogram. That is, the intensity distribution may be determined by computing the histogram and then thresholding the histogram with the predetermined frequency. However, this may be done by grouping the pixels into four quadrants based on their co-ordinates, with centre of the segmented reference RoI as the origin for the quadrants. Consequently, there may be four histograms corresponding to each of the four quadrants and four groups of pixel intensity ranges resulting from thresholding of the histograms. These pixel intensity ranges may be referred as Background range (BGr).

In an embodiment, subsequent to obtaining the Lr and four BGr, the parameter determination module <NUM> may compute a Neighbor Threshold (NT) and a Background Histogram percent (BbHp). In an embodiment, the NT may represent a minimum number of neighboring pixels of a point that must satisfy the Lr and BGr criteria for that point to be shortlisted and/or selected as the reference point. In an embodiment, the BgHp may represent an actual background histogram threshold percentage that may be considered for intensity comparison for shortlisting a relevant reference seed.

In an embodiment, the parameter determination module <NUM> may determine the background parameters using the following method. Initially, for each quadrant, the background histogram may be thresholded with multiple frequency ranges like <NUM>%, <NUM>%, <NUM>% and <NUM>%. Thus, at the end of thresholding, the BGr may be obtained for every frequency threshold for each quadrant of the background pixels. In an embodiment, an overlap between the Lr and each of the BGr may be computed and the values of NT and BgHp may be derived iteratively as illustrated in Table A and Table B below.

In an embodiment, the criteria for deriving parameters may be different for different types and/or sources of the images. For example, the criteria for deriving parameters of images obtained from Computer Tomography (CT) may be as shown in Table A. Similarly, the criteria for deriving parameters of images obtained from Magnetic Resonance Imaging (MRI) may be as shown in Table B.

In an embodiment, the overlap percentage (shown in Table A and Table B) may be the percentage of Lr that is overlapping with BGr. Further, the overlap count (shown in Table B) may be the number of quadrants of BGr that has overlap with Lr.

In an embodiment, the reference point determination module <NUM> may be used for determining the plurality of reference points <NUM> corresponding to the segmented reference RoI based on the one or more morphological characteristics of the segmented reference RoI. In an embodiment, since the longest diameter and the short axis of the segmented reference RoI, determined based on the reference study of the reference image <NUM>, lie on cross section of the RoI, the longest diameter and the short axis may be considered to cover most of the distinct intensity regions in the segmented RoI. Therefore, in an embodiment, the plurality of reference points <NUM> on the segmented reference RoI may be determined as co-ordinates of discrete points identified on the longest diameter and the short axis.

In an embodiment, the process of obtaining the reference RoI and determining the plurality of reference points <NUM> on the reference RoI may be illustrated using exemplary representations in <FIG> - <FIG>.

<FIG> shows a reference image <NUM> comprising a reference RoI <NUM>. <FIG> indicates a longest diameter 303A and a short axis 303B for the segmented reference RoI <NUM>. Now, the plurality of reference points <NUM> corresponding to the segmented reference RoI <NUM> may be determined by identifying a discrete set of points on the longest diameter 303A and the short axis 303B of the segmented reference RoI <NUM>, as shown in <FIG>.

In an embodiment, the seed generation module <NUM> may be used for generating a plurality of translated points <NUM> on the current image <NUM>, in which the Target RoI <NUM> has to be segmented, by translating each of the plurality of reference points <NUM> onto the current image <NUM>. In an embodiment, once all the reference points <NUM> have been determined and extracted from the reference image <NUM>, the seed generation module <NUM> may transform each of the plurality of reference points <NUM> to the current image <NUM> to get the plurality of translated points <NUM> on the current image <NUM>. That is, the reference points <NUM> determined from the reference image <NUM> may be transformed into the current image <NUM> as translated points <NUM>, as shown in <FIG>.

In an embodiment, the seed selection module <NUM> may be used for automatically selecting relevant seeds <NUM> in the current image <NUM>, from the plurality of translated points <NUM>, based on the one or more segmentation parameters <NUM>. <FIG> shows the relevant seeds <NUM> that are shortlisted and selected from the translated points <NUM>. In an embodiment, the pixel intensity ranges corresponding to the segmented reference RoI <NUM> and for each of the four quadrants of the background pixels may be the most critical segmentation parameters <NUM> used for selecting the relevant seeds <NUM> in the current image <NUM>.

In an embodiment, one of the plurality of translated points <NUM> 'P', may be shortlisted as the relevant seed, only if the seed point 'P' satisfies one or more conditions defined below:.

In an embodiment, the Point P may be shortlisted when the point P satisfies all the three conditions <NUM>, <NUM> and <NUM> above or when the point P satisfies only the condition <NUM> above.

In an embodiment, if the point P is not shortlisted, then a neighbor point of the point P may be checked and shortlisted if the neighbor point satisfies the conditions <NUM>, <NUM> and <NUM> above.

In an embodiment, after repeating the above analysis for all the points of the plurality of translated points <NUM>, if none of the plurality of translated points <NUM> get shortlisted, then the translated points <NUM> that satisfy only condition <NUM> may be shortlisted as the relevant seeds <NUM>.

In an embodiment, in case of CT images, the bilateral filtered pixel intensity of point P may be used against all the conditions <NUM>-<NUM> mentioned above. Whereas, for MR images no filtering may be applied and the pixel intensity may be directly compared. Also, in the CT images, the intensities represent a quantitative information and hence two CT scans may be compared directly. However, in MR images, since the intensities of the pixels only represent a qualitative information, the two scans, which are of same variants, may have to be normalized first before comparing.

In an embodiment, the segmentation module <NUM> may be used for performing a multi-seed segmentation of the selected relevant seeds <NUM> for estimating and segmenting a target RoI <NUM> in the current image <NUM>. In an embodiment, the multi-seed segmentation may be considered as an extension of single seed segmentation technique, which is implemented to address heterogeneity of the reference RoI, for all the selected relevant seed points obtained in the above process. In an embodiment, multi-seed segmentation of the selected relevant seeds <NUM> may be performed using one of the existing multi-seed segmentation techniques such as parametric method, level-set method, clustering method and the like. Alternatively, the multi-segmentation may also be performed using a region-growing technique, as illustrated in the present disclosure.

In an embodiment, the segmentation module <NUM> may receive a user input, indicating whether the target RoI <NUM> determined in the current image <NUM> is bigger than, or smaller than or of same size as that of the reference RoI determined in the reference image <NUM>. This user input may be used to limit the area to be searched and/or scanned while performing the multi-seed segmentation.

In an embodiment, if the target RoI <NUM> in the current image <NUM> is bigger than that of the reference RoI, then the search area radius may be set equal to the longest diameter of the reference RoI.

In an embodiment, if the target RoI <NUM> is smaller than that of the reference RoI, then the search area radius may be set to <NUM>% of the longest diameter of the reference lesion.

In an embodiment, if the target RoI <NUM> is of the same size of the reference RoI, then search area radius may be set to <NUM>% of the longest diameter of the reference lesion.

That is, the difference in the size of the target RoI <NUM> and the size of the reference RoI may be correlated and adjusted using one of the above adjustments.

<FIG> indicates the region obtained by performing the multi-seed segmentation of the relevant seeds <NUM> on the current image <NUM>. In an embodiment, the multi-seed segmentation may exclude some areas of the target RoI <NUM>, having dissimilar intensities in comparison to the overall intensity of the RoI, from generating the target RoI <NUM>. However, this may be addressed by using an intensity-based k-means clustering region growing technique in the region defined by the multi-seed segmentation output.

In an embodiment, the region obtained by performing the multi-seed segmentation of the relevant seeds <NUM> may be used to determine an overlapping region <NUM> on the current image <NUM>. Thereafter, the overlapping region <NUM> may be extracted from the current image <NUM> and treated as the target RoI <NUM> corresponding to the current image <NUM>, as show in <FIG>. That is, <FIG> indicates the target RoI <NUM> extracted by performing the above steps on the current image <NUM>.

In an embodiment, a comparison of the reference RoI and the target RoI <NUM>, as shown in <FIG>, helps in determining changes in the RoI across the reference image <NUM> and the current image <NUM> of the same entity.

<FIG> shows a flowchart illustrating a method for automatically propagating segmentation medical images in accordance with some embodiments of the present disclosure.

As illustrated in <FIG>, the method <NUM> includes one or more blocks illustrating a method for automatically propagating segmentation in medical images using an image segmentation system <NUM>, illustrated in <FIG>. The method <NUM> may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, and functions, which perform specific functions or implement specific abstract data types.

The order in which the method <NUM> is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method. Additionally, individual blocks may be deleted. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof.

At block <NUM>, the method <NUM> comprises determining one or more segmentation parameters <NUM> based on analysis of a segmented reference Region of Interest (RoI) in a reference image <NUM>. In an embodiment, determining the one or more segmentation parameters <NUM> may comprise, without limiting to, determining the one or more morphological characteristics of the segmented reference RoI <NUM>, a position of the segmented reference RoI <NUM> within the reference image <NUM>, an intensity distribution of the segmented reference RoI <NUM>, and a background intensity distribution of a background of the segmented reference RoI <NUM>.

In an embodiment, the background intensity distribution of the background may be determined using the following methods:.

In an embodiment, the intensity distribution of the segmented reference RoI <NUM> may be determined by generating a histogram of pixels in the segmented reference RoI <NUM> and determining one or more intensity ranges of the segmented reference RoI <NUM> based on the histogram and a predetermined threshold. In an embodiment, the one or more intensity ranges may be representative of the intensity distribution of the segmented reference RoI <NUM>.

At block <NUM>, the method <NUM> comprises determining a plurality of reference points <NUM> corresponding to the segmented reference RoI <NUM> based on one or more morphological characteristics of the segmented reference RoI <NUM>. In an embodiment, determining the one or more morphological characteristics of the segmented reference RoI <NUM> may comprise, without limiting to, determining a short axis 303B and a longest diameter 303A of the segmented reference RoI <NUM>. Further, determining the one or more morphological characteristics may include determining a size and a shape of the segmented reference RoI <NUM> based on length and coordinates of the short axis 303B and the longest diameter 303A of the segmented reference RoI <NUM>.

At block <NUM>, the method <NUM> comprises generating a plurality of translated points <NUM> on a current image <NUM>, in which a target RoI <NUM> has to be segmented, by translating each of the plurality of reference points <NUM> onto the current image <NUM>. In an embodiment, the reference image <NUM> and the current image <NUM> may be images of a single entity captured at different time periods.

At block <NUM>, the method <NUM> comprises automatically selecting relevant seeds <NUM> in the current image <NUM>, from the plurality of translated points <NUM>, based on the one or more segmentation parameters <NUM>. In an embodiment, the relevant seeds <NUM> in the current image <NUM> are automatically selected by selecting one or more translated points <NUM> from the plurality of translated points <NUM> if an intensity of the one or more translated points <NUM> falls within the intensity distribution of the segmented reference RoI <NUM>. Further, the relevant seeds <NUM> in the current image may be automatically selected based on the following criteria:.

At block <NUM>, the method <NUM> comprises performing a multi-seed segmentation of the selected relevant seeds <NUM> for estimating and segmenting the Target RoI <NUM> in the current image <NUM>. In an embodiment, the target RoI <NUM> is the propagated segmentation of the segmented RoI in the reference image <NUM>. In an embodiment, a prerequisite condition for performing the segmentation of the target RoI <NUM> may be that the pixel intensities of the current image <NUM> must be quantitatively comparable or normalized to pixel intensities of the reference image <NUM>.

<FIG> illustrates a block diagram of an exemplary computer system <NUM> for implementing embodiments consistent with the present disclosure. In an embodiment, the computer system <NUM> may be an image segmentation system <NUM>, which is used for automatically propagating segmentation in medical images. The computer system <NUM> may include a central processing unit ("CPU" or "processor") <NUM>. The processor <NUM> may comprise at least one data processor for executing program components for executing user- or system-generated business processes.

A user may include a person, a patient, a medical practitioner and/or a technologist, a person using the image segmentation system <NUM> and the like. The processor <NUM> may include specialized processing units such as integrated system (bus) controllers, memory management control units, floating point units, graphics processing units, digital signal processing units, etc..

The processor <NUM> may be disposed in communication with one or more input/output (I/O) devices (<NUM> and <NUM>) via I/O interface <NUM>. The I/O interface <NUM> may employ communication protocols/methods such as, without limitation, audio, analog, digital, stereo, IEEE-<NUM>, serial bus, Universal Serial Bus (USB), infrared, PS/<NUM>, BNC, coaxial, component, composite, Digital Visual Interface (DVI), high-definition multimedia interface (HDMI), Radio Frequency (RF) antennas, S-Video, Video Graphics Array (VGA), IEEE <NUM>. n /b/g/n/x, Bluetooth, cellular (e.g., Code-Division Multiple Access (CDMA), High-Speed Packet Access (HSPA+), Global System For Mobile Communications (GSM), Long-Term Evolution (LTE) or the like), etc. Using the I/O interface <NUM>, the computer system <NUM> may communicate with one or more I/O devices <NUM> and <NUM>.

In some embodiments, the processor <NUM> may be disposed in communication with a communication network <NUM> via a network interface <NUM>. The network interface <NUM> may communicate with the communication network <NUM>. The network interface <NUM> may employ connection protocols including, without limitation, direct connect, Ethernet (e.g., twisted pair <NUM>/<NUM>/<NUM> Base T), Transmission Control Protocol/Internet Protocol (TCP/IP), token ring, IEEE <NUM>. 11a/b/g/n/x, etc. Using the network interface <NUM> and the communication network <NUM>, the computer system <NUM> may communicate with a reference database <NUM> for receiving a segmented reference Region of Interest (RoI) of a reference image <NUM>. Further, the communication network <NUM> may be used to receive the reference image <NUM> and a current image <NUM> from one or more sources such as an X-Ray scanner.

The communication network <NUM> can be implemented as one of the several types of networks, such as intranet or Local Area Network (LAN) and such within the organization. The communication network <NUM> may either be a dedicated network or a shared network, which represents an association of several types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), etc., to communicate with each other. Further, the communication network <NUM> may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, etc..

In some embodiments, the processor <NUM> may be disposed in communication with a memory <NUM> (for example, RAM <NUM> and ROM <NUM> as shown in <FIG>) via a storage interface <NUM>. The storage interface <NUM> may connect to memory <NUM> including, without limitation, memory drives, removable disc drives, etc., employing connection protocols such as Serial Advanced Technology Attachment (SATA), Integrated Drive Electronics (IDE), IEEE-<NUM>, Universal Serial Bus (USB), fiber channel, Small Computer Systems Interface (SCSI), etc. The memory drives may further include a drum, magnetic disc drive, magneto-optical drive, optical drive, Redundant Array of Independent Discs (RAID), solid-state memory devices, solid-state drives, etc..

The memory <NUM> may store a collection of program or database components, including, without limitation, user/application <NUM>, an operating system <NUM>, a web browser <NUM>, and the like. In some embodiments, computer system <NUM> may store user/application data <NUM>, such as the data, variables, records, and the like as described in this disclosure. Such databases may be implemented as fault-tolerant, relational, scalable, secure databases such as Oracle or Sybase.

The operating system <NUM> may facilitate resource management and operation of the computer system <NUM>. Examples of operating systems include, without limitation, Apple Macintosh OS X, UNIX, Unix-like system distributions (e.g., Berkeley Software Distribution (BSD), FreeBSD, Net BSD, Open BSD, etc.), Linux distributions (e.g., Red Hat, Ubuntu, K-Ubuntu, etc.), International Business Machines (IBM) OS/<NUM>, Microsoft Windows (XP, Vista/<NUM>/<NUM>, etc.), Apple iOS, Google Android, Blackberry Operating System (OS), or the like.

A user interface may facilitate display, execution, interaction, manipulation, or operation of program components through textual or graphical facilities. For example, user interfaces may provide computer interaction interface elements on a display system operatively connected to the computer system <NUM>, such as cursors, icons, check boxes, menus, windows, widgets, etc. Graphical User Interfaces (GUIs) may be employed, including, without limitation, Apple Macintosh operating systems' Aqua, IBM OS/<NUM>, Microsoft Windows (e.g., Aero, Metro, etc.), Unix X-Windows, web interface libraries (e.g., ActiveX, Java, JavaScript, AJAX, HTML, Adobe Flash, etc.), or the like.

Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present invention. The term "computer-readable medium" should be understood to include tangible items and exclude carrier waves and transient signals, i.e., non-transitory. Examples include Random Access Memory (RAM), Read-Only Memory (ROM), volatile memory, nonvolatile memory, hard drives, Compact Disc (CD) ROMs, Digital Video Disc (DVDs), flash drives, disks, and any other known physical storage media.

The terms "an embodiment", "embodiment", "embodiments", "the embodiment", "the embodiments", "one or more embodiments", "some embodiments", and "one embodiment" mean "one or more (but not all) embodiments of the invention(s)" unless expressly specified otherwise.

The terms "including", "comprising", "having" and variations thereof mean "including but not limited to", unless expressly specified otherwise. The enumerated listing of items does not imply that any or all the items are mutually exclusive, unless expressly specified otherwise.

The terms "a", "an" and "the" mean "one or more", unless expressly specified otherwise. A description of an embodiment with several components in communication with each other does not imply that all such components are required. On the contrary, a variety of optional components are described to illustrate the wide variety of possible embodiments of the invention.

When a single device or article is described herein, it will be clear that more than one device/article (whether they cooperate) may be used in place of a single device/article. Similarly, where more than one device or article is described herein (whether they cooperate), it will be clear that a single device/article may be used in place of the more than one device or article or a different number of devices/articles may be used instead of the shown number of devices or programs. The functionality and/or the features of a device may be alternatively embodied by one or more other devices which are not explicitly described as having such functionality/features. Thus, other embodiments of the invention need not include the device itself.

Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope of the invention be limited not by this detailed description, but rather by any claims that issue on an application based here on. Accordingly, the embodiments of the present invention are intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims.

While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting.

Claim 1:
A computer-implemented
method of automatically propagating segmentation in a medical image, the method comprising:
determining one or more segmentation parameters based on analysis of a segmented reference Region of Interest (RoI) in a reference image, wherein determining the one or more segmentation parameters comprises determining the one or more morphological characteristics of the segmented reference RoI, and wherein determining the one or more morphological characteristics of the segmented reference RoI comprises determining a short axis and a longest diameter of the segmented reference RoI;
determining a plurality of reference points corresponding to the segmented reference RoI based on the one or more morphological characteristics of the segmented reference RoI;
generating a plurality of translated points on a current image, in which a target RoI has to be segmented, by translating each of the plurality of reference points onto the current image;
automatically selecting relevant seeds in the current image, from the plurality of translated points, based on the one or more segmentation parameters, wherein the selection comprises selecting one or more translated points from the plurality of translated points if an intensity of the one or more translated points falls within the intensity distribution of the segmented reference RoI; and
performing a multi-seed segmentation of the selected relevant seeds for estimating and segmenting the target RoI in the current image, wherein the target RoI is the propagated segmentation of the segmented RoI in the reference image and wherein pixel intensities of the current image are quantitatively comparable or can be normalized to pixel intensities of the reference image.