TECHNICAL FIELD

Embodiments of the disclosure generally relate to the field of medical diagnostics. In particular, embodiments of the disclosure relate to systems, methods, and computer program products for accurate segmentation and approximation of a tumor (e.g., brain tumor) in MRI imaging.

BACKGROUND

Gliomas are classified as Grades I to IV based on histology and clinical criteria. Grade I tumors are generally benign. Adult Grade II tumors are low-grade gliomas (LGGs) and include astrocytomas and oligodendrogliomas. Essentially, all Grade II lesions eventually progress to high-grade glioma (HGG) (Grade III/IV). High-grade gliomas are diffusely infiltrating tumors. To study tumor growth dynamics, surgical results, and tumor response after oncological treatment, the accurate assessment of tumor volume on brain MRIs is crucial. By convention, tumor appearance on contrast enhanced T1-weighted magnetic resonance images (MRIs) defines the tumor volume in quantitative studies, but methods of assessment vary much between studies. Additionally, the importance of measuring the non-enhancing component of the tumor is now recognized.

High-grade glioma tumors on brain MRIs may consist of two or more sub-components and these sub-components may be defined based on known clinical standard of differences in MRI signal intensities on 4 distinct MRI sequences (T1 weighted pre-contrast, T1 weighted post-contrast, T2 weighted and FLAIR) that are routinely acquired as part of everyday clinical practice.

Manual volume segmentation remains the gold standard for volume measurement. However, this is very time-consuming (45-60 minutes per MRI on average) and suffers from significant inter-rater and intra-rater variabilities. Tumor volumes are important to inform disease status during treatment through the Modified Response Assessment in Neuro-oncology (e.g., using the RANO criteria).

RANO (Response Assessment in NeuroOncology) is the international standard to determine tumor response to treatment. Using RANO improves median progression free survival (PFS) and objective response rates (ORRs). 3-D volumetric analysis is proven to be superior to the current 2-D method. Routine manual 2D analysis of brain tumors is very complicated, time consuming and inadequate for evaluating tumors, especially tumors with complex geometric shapes. Due to these limitations, RANO is not routinely followed. Current manual technique involves determining sum of the products of diameters (SPD) from one MRI slice instead of volume, which is analogous to measuring an iceberg by its tip.FIG.1shows an example where manual tumor volume annotation (i.e., white perpendicular crossing lines) missed a large portion of the adjacent tumor volume.

Accordingly, there is a need for improved methods and systems for semi-automatic and automatic segmentation to save time and resources in making volumetric measurements of different compartments of tumors (e.g., high-grade brain glioma).

BRIEF SUMMARY

Systems, methods, and computer program products are provided for segmenting a brain tumor from various MRI sequencing techniques. In various embodiments, a method is provided where a plurality of MRI sequences of a head of a patient are received. Each MRI sequence includes a T1-weighted with contrast image, a Fluid Attenuated Inversion Recovery (FLAIR) image, a T1-weighted image, and a T2-weighted image. Each image of the plurality of MRI sequences is registered to an anatomical atlas. A plurality of modified MRI sequences are generated by removing a skull from each image in the plurality of MRI sequences. A tumor segmentation map is determined by segmenting a tumor within a brain in each image in the plurality of modified MRI sequences. The tumor segmentation map is applied to each of the plurality of MRI sequences to thereby generate a plurality of labelled MRI sequences.

In various embodiments, a system includes a medical image database and a computing node including a computer readable storage medium having program instructions embodied therewith. The program instructions are executable by a processor of the computing node to cause the processor to perform a method where a plurality of MRI sequences of a head of a patient are received. Each MRI sequence includes a T1-weighted with contrast image, a Fluid Attenuated Inversion Recovery (FLAIR) image, a T1-weighted image, and a T2-weighted image. Each image of the plurality of MRI sequences is registered to an anatomical atlas. A plurality of modified MRI sequences are generated by removing a skull from each image in the plurality of MRI sequences. A tumor segmentation map is determined by segmenting a tumor within a brain in each image in the plurality of modified MRI sequences. The tumor segmentation map is applied to each of the plurality of MRI sequences to thereby generate a plurality of labelled MRI sequences.

In various embodiments, a computer program product includes a computer readable storage medium having program instructions embodied therewith. The program instructions are executable by a processor to cause the processor to perform a method where a plurality of MRI sequences of a head of a patient are received. Each MRI sequence includes a T1-weighted with contrast image, a Fluid Attenuated Inversion Recovery (FLAIR) image, a T1-weighted image, and a T2-weighted image. Each image of the plurality of MRI sequences is registered to an anatomical atlas. A plurality of modified MRI sequences are generated by removing a skull from each image in the plurality of MRI sequences. A tumor segmentation map is determined by segmenting a tumor within a brain in each image in the plurality of modified MRI sequences. The tumor segmentation map is applied to each of the plurality of MRI sequences to thereby generate a plurality of labelled MRI sequences.

