1. A computer implemented integrated system (100) for brain tumor analysis using multi-sequence magnetic resonance imaging (102) (MRI), the system comprising: a. a plurality of MRI sequences of a subject brain including different imaging modalities; b. the MRI sequences are processed to achieve spatial alignment and intensity normalization (104); c. feature extraction (106) from each of the MRI sequences; d. a spatial contextual feature fusion (110) is performed by combining the extracted features (106) while incorporating relationships between neighboring regions across the MRI sequences; e. a context-aware learning (108) is applied on the fused features to capture dependencies between tumor regions and surrounding brain tissues; f. the learned features are used to simultaneously perform tumor segmentation (112), precise delineation of tumor boundaries (114), and heterogeneity Mapping (116) within the tumor; and g. wherein the generating output data representing segmented tumor regions, defined tumor boundaries, and identification of internal tumor variations.

2. The system (100) as claimed in claim 1, wherein the plurality of MRI sequences comprises T1-weighted, T2-weighted, and FLAIR images.

3. The system (100) as claimed in claim 1, wherein the spatial alignment includes registration of all MRI sequences to a common reference space.

4. The system (100) as claimed in claim 1, wherein the intensity normalization (104) is performed to reduce variations across different MRI modalities.

5. The system (100) as claimed in claim 1, wherein the feature extraction (106) is carried out using one or more deep learning-based models.

6. The system (100) as claimed in claim 1, wherein the spatial contextual feature fusion (110) incorporates neighborhood information of adjacent regions to improve feature representation.

7. The system (100) as claimed in claim 1, wherein the context-aware learning (108) models spatial and structural relationships between tumor regions and surrounding brain tissues.

8. The system (100) as claimed in claim 1, wherein the heterogeneity mapping (116) identifies different internal tumor regions including active regions, necrotic regions, and edema.

9. The system (100) as claimed in claim 1, wherein the data augmentation techniques including rotation, scaling, flipping, and noise addition to improve robustness.

10. The system (100) as claimed in claim 1, wherein the tumor segmentation (112), delineation boundaries (114), and heterogeneity mapping (116) are performed simultaneously in a single integrated process.  

6. DATE AND SIGNATURE Dated this 06th April, 2026 Signature (Mr. Srinivas Maddipati) IN/PA 3124 Agent for Applicant.

Description:4. DESCRIPTION
Technical Field of the invention
The present invention relates to the field of medical image processing and artificial intelligence. More particularly, the invention relates to automated analysis of multi-sequence MRI images for brain tumor detection, segmentation, and characterization using advanced computational techniques.

Background of the invention

Brain tumors are serious medical conditions that need accurate diagnosis for proper treatment. MRI scans are commonly used because they show detailed images of the brain. However, analyzing these scans manually takes time and depends on the experience of the doctor, which can sometimes lead to differences in results.

Automated systems have been developed to detect tumors from MRI images, with most of these systems mainly focusing on identifying the tumor area (segmentation). However, many of them use only one type of MRI image or limited data, which makes it difficult to fully understand the tumor’s size, shape, and internal structure.

Existing methods usually handle tasks like tumor detection, boundary identification, and internal analysis separately. Because of this, the overall process becomes less efficient and may not provide a complete picture of the tumor in a single step.

Many systems do not properly consider the relationship between nearby regions in the brain and often analyze each part of the image separately, which can reduce accuracy in complex cases. In addition, limited medical data and variations in MRI images affect performance, as models without proper training techniques such as data augmentation may not work well on new or different data.
Accordingly, there exists a need for a simple and accurate integrated system that can analyze multiple MRI images together, detect the tumor, clearly define its boundaries, and identify variations within it in a single step. The system should also use spatial and contextual information along with effective training methods to provide reliable and consistent results.
Objects of the invention

The principal object of the present invention is to provide an integrated system for accurate brain tumor segmentation, precise delineation, and heterogeneity detection using multi-sequence MRI images.

Another object of the present invention is to combine multiple MRI sequences to capture complete and detailed information about the tumor.

Another object of the present invention is to improve accuracy by using spatial and contextual information of surrounding brain regions.

Another object of the present invention is to automatically identify tumor boundaries clearly and reliably.

Another object of the present invention is to detect different regions within the tumor to support better analysis and treatment planning.

Another object of the present invention is to enhance system performance and consistency using advanced data augmentation techniques.
Brief Summary of the invention

The present invention provides an integrated approach for analyzing brain tumors using multi-sequence MRI images, where different types of MRI scans are used together to obtain a more complete and detailed understanding of the tumor, as each sequence highlights different characteristics of the brain tissue.

The MRI images are preprocessed to ensure proper alignment and consistency across all sequences, and suitable data augmentation techniques such as rotation, scaling, and noise addition are applied to improve data quality and variation, thereby enhancing the reliability of the analysis.

Features are extracted from each MRI sequence and combined using spatial feature fusion, wherein not only individual image points are considered but also the relationship between neighboring regions is analyzed to better capture the shape and structure of the tumor.

Context-aware learning is applied to understand how the tumor relates to surrounding brain tissues, enabling the model to make more accurate predictions and reduce errors, especially in complex or unclear cases.

The integrated framework enables simultaneous performance of multiple tasks, including tumor detection, clear boundary delineation, and identification of different internal regions within the tumor, such as active areas and other variations.

The system provides a simple, accurate, and reliable solution for brain tumor analysis by effectively combining multiple MRI inputs and advanced learning techniques. It reduces dependency on manual interpretation, minimizes chances of human error, and ensures consistent results. By clearly identifying the tumor, its boundaries, and internal variations, it supports doctors in making better decisions for diagnosis, treatment planning, and patient management.

Brief Description of the drawings

The invention will be further understood from the following detailed description of a preferred embodiment taken in conjunction with an appended drawing, in which:

Fig. 1 illustrates a vertical flowchart of a multi-sequence MRI-based brain tumor analysis system (100), in accordance with an exemplary embodiment of the present invention;

Detailed Description of the invention

It is to be understood that the present disclosure is not limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the drawings. The present disclosure is capable of other embodiments and of being practiced or of being carried out in various ways. In addition, it is to be understood that the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting.

The use of “including”, “comprising” or “having” and variations thereof herein is meant to encompass the items listed thereafter and equivalents thereof as well as additional items. The terms “a” and “an” herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items. Further, the use of terms “first”, “second”, and “third”, and the like, herein do not denote any order, quantity, or importance, but rather are used to distinguish one element from another.

According to an exemplary embodiment of the present invention, an integrated system is provided for analyzing brain tumors using multi-sequence MRI images, wherein different MRI sequences provide varied information about brain tissues, and their combined use enables a more complete and accurate understanding of the tumor.

In accordance with an exemplary embodiment of the present invention, wherein the plurality of MRI sequences of a subject brain, including different imaging modalities, are received as input, and such sequences may include T1-weighted, T2-weighted, and FLAIR images, each highlighting specific characteristics of the brain and tumor regions.

In accordance with an exemplary embodiment of the present invention, wherein the MRI sequences are processed to achieve spatial alignment and intensity normalization, such that all images are properly matched and variations in intensity are reduced to ensure consistency for further analysis.

In accordance with an exemplary embodiment of the present invention, wherein the features are extracted from each of the MRI sequences, said features representing important information about the tumor and surrounding brain tissues for accurate analysis.

In accordance with an exemplary embodiment of the present invention, wherein the extracted features are combined using spatial contextual feature fusion, such that features from multiple MRI sequences are merged while also considering relationships between neighboring regions to better capture tumor structure.

In accordance with an exemplary embodiment of the present invention, wherein the context-aware learning is applied to the fused features to capture dependencies between tumor regions and surrounding brain tissues, thereby improving accuracy in identifying complex tumor patterns.

In accordance with an exemplary embodiment of the present invention, wherein the learned features are used to simultaneously perform tumor segmentation, precise delineation of tumor boundaries, and heterogeneity detection to identify different internal regions within the tumor.

In accordance with an exemplary embodiment of the present invention, wherein output data is generated representing segmented tumor regions, clearly defined tumor boundaries, and identification of internal tumor variations for further clinical use.

In accordance with an exemplary embodiment of the present invention, wherein the integrated system provides a simple, accurate, and reliable solution for brain tumor analysis, reduces manual effort, and supports improved diagnosis and treatment planning.

Referring to figures, Figure 1 illustrates a vertical workflow for brain tumor analysis system (100) using multi-sequence MRI (102) inputs. The input images are first preprocessed through spatial alignment and normalization (104), followed by multi-modal feature extraction (106). The extracted features are then used for tumor segmentation (112), delineation boundaries (114), and heterogeneity mapping (116). Context-aware learning (108) and spatial contextual feature fusion (110) are applied to enhance the analysis. The final output includes segmented tumor (112) regions, delineated boundaries (114), and heterogeneity mapping (116), which are used for diagnostic and treatment planning.
, Claims:CLAIMS
I/We Claim:
1. A computer implemented integrated system (100) for brain tumor analysis using multi-sequence magnetic resonance imaging (102) (MRI), the system comprising:
a. a plurality of MRI sequences of a subject brain including different imaging modalities;
b. the MRI sequences are processed to achieve spatial alignment and intensity normalization (104);
c. feature extraction (106) from each of the MRI sequences;
d. a spatial contextual feature fusion (110) is performed by combining the extracted features (106) while incorporating relationships between neighboring regions across the MRI sequences;
e. a context-aware learning (108) is applied on the fused features to capture dependencies between tumor regions and surrounding brain tissues;
f. the learned features are used to simultaneously perform tumor segmentation (112), precise delineation of tumor boundaries (114), and heterogeneity Mapping (116) within the tumor; and
g. wherein the generating output data representing segmented tumor regions, defined tumor boundaries, and identification of internal tumor variations.

2. The system (100) as claimed in claim 1, wherein the plurality of MRI sequences comprises T1-weighted, T2-weighted, and FLAIR images.

3. The system (100) as claimed in claim 1, wherein the spatial alignment includes registration of all MRI sequences to a common reference space.
4. The system (100) as claimed in claim 1, wherein the intensity normalization (104) is performed to reduce variations across different MRI modalities.

5. The system (100) as claimed in claim 1, wherein the feature extraction (106) is carried out using one or more deep learning-based models.

6. The system (100) as claimed in claim 1, wherein the spatial contextual feature fusion (110) incorporates neighborhood information of adjacent regions to improve feature representation.

7. The system (100) as claimed in claim 1, wherein the context-aware learning (108) models spatial and structural relationships between tumor regions and surrounding brain tissues.

8. The system (100) as claimed in claim 1, wherein the heterogeneity mapping (116) identifies different internal tumor regions including active regions, necrotic regions, and edema.

9. The system (100) as claimed in claim 1, wherein the data augmentation techniques including rotation, scaling, flipping, and noise addition to improve robustness.

10. The system (100) as claimed in claim 1, wherein the tumor segmentation (112), delineation boundaries (114), and heterogeneity mapping (116) are performed simultaneously in a single integrated process.
 
6. DATE AND SIGNATURE

Dated this 06th April, 2026

Signature

(Mr. Srinivas Maddipati)
IN/PA 3124
Agent for Applicant.