Method of automatically modifying imaging range in biological sample microscopic imaging

Abstract

A method for automatically altering an imaging area in microscopic imaging of a biological sample. In the method, a sample outline is differentiated from surrounding tissues by means of endogenous or exogenous markers; an initial sample imaging area is set; optical microscopic imaging is performed on a sample surface layer, wherein the imaging area is larger than an area to be imaged of the sample; an actual sample area is calculated by an outline identification algorithm using an imaging result of the sample surface layer and is set as an imaging area of next layer; optical microscopic imaging is performed on a sample to be imaged of the next layer according to the set imaging area, wherein the imaging area covers the area to be imaged of the sample and no redundant imaging is performed; and the above steps are repeated until a data acquisition task is completed.

Claims

1. A method for automatically altering an imaging area in microscopic imaging of a biological sample, comprising: (1) labeling an outline of the biological sample to differentiate the biological sample from a surrounding embedding medium; (2) setting an initial sample imaging area; imaging a sample surface layer using optical microscopy; wherein the initial sample imaging area is larger than an area to be imaged of the biological sample; (3) calculating an actual sample area using an outline identification algorithm based on an imaging result of the sample surface layer and setting the actual sample area as an imaging area of a next layer; (4) imaging a sample to be imaged of the next layer using optical microscopy based on the imaging area of the next layer set at step (3); wherein the imaging area of the next layer covers the area to be imaged of the biological sample, and there is no redundant imaging; (5) repeating steps (3) and (4) until data acquisition is completed.

2. The method of claim 1, wherein the outline identification algorithm in step (3) further comprises: (a) filtering the imaging result of the sample surface layer; (b) setting a threshold according to sample type, binarizing the filtered image to obtain an effective sample area; and (c) judging whether the effective sample area is correct; if the effective sample area is not correct, altering the threshold according to error type and returning to step (b); if the effective sample area is correct, proceeding to next step.

3. The method of claim 2, wherein morphological operations of expansion, corrosion and area filling are performed on the binarized image and non-sample area is eliminated, so that a fluorescence signal emitted by the embedding medium is differentiated from a signal from the effective sample area.

4. The method of claim 2, wherein the step of setting the threshold according to sample type comprises setting various thresholds corresponding to the sample type; or, calculating all possible thresholds and averaging the thresholds with an area change of the effective sample area less than 1%; or, determining appropriate thresholds of the sample according to the area change of the effective sample area.

5. The method of claim 3, wherein the step of setting the threshold according to sample type comprises setting various thresholds corresponding to the sample type; or, calculating all possible thresholds and averaging the thresholds with an area change of the effective sample area less than 1%; or, determining appropriate thresholds of the sample according to the area change of the effective sample area.

6. The method of claim 4, wherein the threshold for binarization is increased if a boundary of the identified sample area overlaps with a boundary of the imaging area.

7. The method of claim 4, wherein the threshold for binarization is reduced if the number of the identified sample area is greater than actual number of possible sample area.

8. The method of claim 4, wherein the threshold for binarization is reduced if no effective sample area is identified.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) FIG. 1 is a flow chart of a method for automatically altering an imaging area in microscopic imaging of a biological sample.

(2) FIG. 2 shows an imaging area of the biological sample identified and determined by the method of the present invention.

DETAILED DESCRIPTION OF EMBODIMENTS

(3) The present disclosure will be further described below with reference to specific embodiments.

(4) FIG. 1 is a flow chart of a method for automatically altering an imaging area in microscopic imaging of a biological sample. Particularly, The method includes the following steps:

(5) S101: Labeling an outline of the biological sample by an endogenous or exogenous marker to differentiate the biological sample from a surrounding embedding medium. For example, the biological sample is dyed by a dye or other labeling methods, so that cells in the biological sample are enabled to emit enough signals used to differentiate an effective sample area from the embedding medium.

(6) S102: Imaging a sample surface layer using an optical microscopic imaging system according to a set imaging area; sampling and filtering the imaging result of the sample surface layer if necessary. For example, an average filtering or a median filtering is adopted to reduce effects caused by pattern noise.

(7) S103: Setting a threshold according to sample type; binarizing the filtered image to obtain the effective sample area.

(8) S104: Performing morphological operations of expansion, corrosion and area filling on the binarized image; eliminating non-sample area and retaining the effective sample area if necessary, so that a fluorescence signal emitted by the embedding medium is differentiated from a signal from the effective sample area. If physical properties of the embedding medium change a little in the imaging process, the morphological operations of expansion, corrosion and area filling to eliminate the non-sample area are unnecessary.

(9) S105: Judging whether the effective sample area is correct. If the effective sample area is not correct, altering the threshold according to error type and returning to S104; if the effective sample area is correct, proceeding to next step. Specifically, if a boundary of the identified sample area overlaps with a boundary of the imaging area, the threshold for binarization is increased before returning to S104; if the number of the identified sample area is greater than the actual number of possible sample areas or no effective sample area is identified, the threshold for binarization is reduced before returning to S104; otherwise S106 is executed. The thresholds can be set as various values corresponding to the sample type, or all possible thresholds can be calculated, and the thresholds where an area change of the effective sample area is less than 1% are averaged. Due to a great difference in brightness between the sample area and the non-sample area, appropriate thresholds of the sample can be determined according to the area change of the effective sample area.

(10) S106: Detecting whether the data acquisition has been completed, if not, returning to S102 to continue imaging, otherwise, proceeding to next step.

(11) S107: Stopping acquisition.

(12) The above operation process can be automatically operated by computer softwares. FIG. 2 shows an imaging area of the biological sample identified and determined by the method of the above embodiment. In this embodiment, PI is adopted to dye a mouse brain to obtain an outline information of the sample. The sample acquired by a structured light system undergoes median filtering of 5*5, and then a 8-bit image is binarized with a threshold of 20. After eliminating trivial areas in the binarized image, the expansion and area filling are performed, and then the corrosion is performed three times. Templates are all rectangles with a length of 20. After eliminating areas whose gravities are far from a center of the 8-bit image, the expansion operation were performed two times, and the imaging area is restored back to an original size. Then the effective sample area is obtained. In FIG. 2, a white outline is used to represent the effective sample area, which is used as a acquisition area of a next layer of the sample. It can be seen that this method can effectively identify the sample areas, reduce an acquisition of redundant mosaics, and avoid the negative effect of a extension of acquisition time caused by the acquisition of redundant data. At the same time, the method is short time consuming and can meet the requirements of real-time modification area.

(13) It can be appreciated that for one of ordinary skill in the art, improvements or variations can be made based on the above descriptions, and these improvements and variations fall within the scope of the appended claims.

(14) The embodiments are only illustrative of the present disclosure, and apparently the implementations are not limited by the above modes. The embodiments described herein and various modifications based on the spirit of the present disclosure fall within the scope of the present application.